Logistics Management: Contributions of the Section Logistics of the German Academic Association for Business Research, 2023, Dresden, Germany (Lecture Notes in Logistics) 3031381440, 9783031381447

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Lecture Notes in Logistics Series Editors: Uwe Clausen · Michael ten Hompel · Robert de Souza

Udo Buscher Janis S. Neufeld Rainer Lasch Jörn Schönberger   Editors

Logistics Management Contributions of the Section Logistics of the German Academic Association for Business Research, 2023, Dresden, Germany

Lecture Notes in Logistics Series Editors Uwe Clausen, Institute of Transport Logistics, TU Dortmund University, Dortmund, Germany Michael ten Hompel, Fraunhofer Institute for Material Flow and Logistics IML, Dortmund, Germany Robert de Souza, The Logistics Institute - Asia Pacific, National University of Singapore, Singapore, Singapore

Lecture Notes in Logistics (LNL) is a book series that reports the latest research and developments in Logistics, comprising: • • • • • • • • • • • • • • • • • • •

supply chain management transportation logistics intralogistics production logistics distribution systems inventory management operations management logistics network design factory planning material flow systems physical internet warehouse management systems maritime logistics aviation logistics multimodal transport reverse logistics waste disposal logistics storage systems logistics IT

LNL publishes authored conference proceedings, contributed volumes and authored monographs that present cutting-edge research information as well as new perspectives on classical fields, while maintaining Springer’s high standards of excellence, the content is peer reviewed. Also considered for publication are lecture notes and other related material of exceptionally high quality and interest. The subject matter should be original and timely, reporting the latest research and developments in all areas of logistics. The target audience of LNL consists of advanced level students, researchers, as well as industry professionals working at the forefront of their fields. Much like Springer’s other Lecture Notes series, LNL will be distributed through Springer’s print and electronic publishing channels. Indexed by Scopus (2021)

Udo Buscher · Janis S. Neufeld · Rainer Lasch · Jörn Schönberger Editors

Logistics Management Contributions of the Section Logistics of the German Academic Association for Business Research, 2023, Dresden, Germany

Editors Udo Buscher Faculty of Business and Economics TU Dresden Dresden, Sachsen, Germany Rainer Lasch Faculty of Business and Economics TU Dresden Dresden, Sachsen, Germany

Janis S. Neufeld Faculty of Business and Economics TU Dresden Dresden, Sachsen, Germany Jörn Schönberger “Friedrich List” Faculty of Transport and Traffic Sciences TU Dresden Dresden, Sachsen, Germany

ISSN 2194-8917 ISSN 2194-8925 (electronic) Lecture Notes in Logistics ISBN 978-3-031-38144-7 ISBN 978-3-031-38145-4 (eBook) https://doi.org/10.1007/978-3-031-38145-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book contains selected papers presented at the 13th Logistics Management conference (LM 2023) of the Scientific Commission for Logistics (WK-LOG) of the German Academic Association for Business Research (VHB). The LM conference series is continued every two years at different places in Germany. It aims at providing a forum for scientists and practitioners in business administration, IT, and industrial engineering to present and discuss new ideas and technical developments related to the management of logistic systems. Previous LM conferences were held in Bremen (1999, 2013), Aachen (2001), Braunschweig (2003, 2015), Dresden (2005, 2021 (digital)), Regensburg (2007), Hamburg (2009), Bamberg (2011), Stuttgart (2017), and Halle (Saale) (2019). LM 2023 was hosted by the Technische Universität Dresden. It took place from September 13 to 15, 2023. The LM 2023 conference concerns itself with the current general dynamics and challenges in the field of logistics management. To give an insight into the field, LM 2023 has invited two keynote speakers to examine ongoing developments: • Mike Hewitt (Loyola University Chicago) • Helmut Prieschenk (WITRON Logistik + Informatik GmbH) Of the papers presented at the conference, 13 were included as full papers in this proceedings volume. These papers have been selected in a careful review process involving two referees for each paper in up to three rounds of revision. The accepted full papers address a broad spectrum of facets of logistic systems with regard to sustainability, mobility, and optimization. This book is divided into four parts: invited contribution, supply chain management, transport and mobility, and supply chain operations. We hope it provides insights into the state of the art of logistics management and, thus, stimulates future research. September 2023

Udo Buscher Janis S. Neufeld Rainer Lasch Jörn Schönberger

Acknowledgements

The editors express their gratitude to all of the authors and to everybody who has contributed to this volume. In particular, we thank Springer for the easy and uncomplicated collaboration in the editing and publishing process. Our special gratitude goes to DHL for awarding the best full paper contribution of LM 2023. Furthermore, we gratefully acknowledge the efforts of the program committee for reviewing the contributions submitted to the conference. The LM 2023 program committee consists of Prof. Dr. Christian Bierwirth, Martin-Luther-Universität Halle-Wittenberg Jun.-Prof. Dr. Tristan Becker, Technische Universität Dresden Prof. Dr. Ronald Bogaschewsky, Julius-Maximilians-Universität Würzburg Prof. Dr. Udo Buscher, Technische Universität Dresden Prof. Dr. Jan Dethloff, Hochschule Bremen Prof. Dr. Jan Fabian Ehmke, Universität Wien Prof. Dr. Michael Eßig, Universität der Bundeswehr München Prof. Dr. Kathrin Fischer, Technische Universität Hamburg-Harburg Prof. Dr. Hans-Dietrich Haasis, Universität Bremen Prof. Dr. Jochen Gönsch, Universität Duisburg-Essen Prof. Dr. Gudrun P. Kiesmüller, Technische Universität München Prof. Dr. Natalia Kliewer, Freie Universität Berlin Prof. Dr. Matthias Klumpp, Georg-August-Universität Göttingen Prof. Dr. Herbert Kopfer, Universität Bremen Prof. Dr. Herbert Kotzab, University of North Florida Prof. Dr. Anne Lange, Frankfurt UAS Prof. Dr. Rudolf Large, Universität Stuttgart Prof. Dr. Rainer Lasch, Technische Universität Dresden Prof. Dr. Michael Manitz, Universität Duisburg-Essen Prof. Dr. Dirk C. Mattfeld, Technische Universität Braunschweig Prof. Dr. Frank Meisel, Christian-Albrechts-Universität zu Kiel PD Dr. Janis Neufeld, Technische Universität Dresden Prof. Dr. Katja Schimmelpfeng, Universität Hohenheim Prof. Dr. Thorsten Schmidt, Technische Universität Dresden Prof. Dr. Jörn Schönberger, Technische Universität Dresden Prof. Dr. Stefan Seuring, Universität Kassel Prof. Dr. Thomas S. Spengler, Technische Universität Braunschweig Prof. Dr. Wolfgang Stölzle, Universität St. Gallen Prof. Dr. Axel Tuma, Universität Augsburg Prof. Dr. Guido Voigt, Universität Hamburg Prof. Dr. Carl Marcus Wallenburg, WHU—Otto Beisheim School of Management

Contents

Invited Contribution Consolidation-Based Modeling for the Scheduled Service Network Design Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mike Hewitt

3

Supply Chain Management Evaluation of Hydrogen Supply Options for Sustainable Aviation . . . . . . . . . . . . . Karen Ohmstede, Christian Thies, Alexander Barke, and Thomas S. Spengler

27

Pricing and Greening Level Decisions in a Two-Stage Hydrogen Supply Chain Considering State Subsidies and Taxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Beranek, Anna Schütze, and Udo Buscher

43

Green Hydrogen Supply Chains in Latin America – A Research Approach for Partnership Projects with Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Silvia Guillen Suarez, Tobias Witt, Nadja Schlauch, and Matthias Klumpp

64

Designing Pipeline Networks for Carbon Capture and Storage of CO2 -Sources in Germany: An Industry Perspective . . . . . . . . . . . . . . . . . . . . . . Anders Bennæs, Martin Skogset, Tormod Svorkdal, Kjetil Fagerholt, Lisa Herlicka, Frank Meisel, and Wilfried Rickels Engineering Change Management – An Empirical Study on IT, Processual, and Organizational Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas Gollmann, Raphaela Gangl, and Tim Gruchmann

82

99

Transport and Mobility Urban Mobility and Logistics - Past, Present, and Future . . . . . . . . . . . . . . . . . . . . 115 Catherine Cleophas and Frank Meisel Heterogeneous Rail Supply Chain Strategies for International Rail Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Jing Shan and Jörn Schönberger

x

Contents

Collaboration Benefits in Port Hinterland Transportation . . . . . . . . . . . . . . . . . . . . 146 Nicolas Rückert, Kathrin Fischer, Pauline Reinecke, and Thomas Wrona Supply Chain Operations Lot Streaming in Hybrid Flow Shop Manufacturing Systems . . . . . . . . . . . . . . . . . 165 Janis S. Neufeld, Söhnke Maecker, Liji Shen, Rubén Ruiz, and Udo Buscher Carbon-Efficient Scheduling in Distributed Permutation Flow Shops An Analysis of Cause-Effect Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Martin Schönheit Energy-Efficient Production Scheduling: Insides from Academia and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Darleen Dolch and Rainer Lasch A Dirty Little Secret? Conducting a Systematic Literature Review Regarding Overstocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Björn Asdecker, Manette Tscherner, Nikolas Kurringer, and Vanessa Felch Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Invited Contribution

Consolidation-Based Modeling for the Scheduled Service Network Design Problem Mike Hewitt(B) Loyola University Chicago, Chicago, IL, USA [email protected]

Abstract. We review a newly proposed modeling approach for optimizing the transportation of goods within a freight transportation network. Unlike classical modeling strategies, which measure vehicle capacity needs based on the flow of shipments on a time expanded network, this approach is based on explicit consolidations of shipments. Such an approach facilitates modeling multiple operational issues, including complicated loading constraints and piecewise linear cost functions. It also leads to a significantly stronger formulation of the problem, which can in turn be easier to solve than a formulation based on the classical approach.

1

Introduction

A significant segment of the freight transportation market considers the intercity transportation of shipments that are small relative to vehicle capacity. Key to the profitability of carriers serving this market is the ability to consolidate multiple shipments into the same vehicle dispatch. Doing so increases vehicle utilization and the revenues generated from vehicle transportation, which typically comes at a fixed cost. Achieving such consolidation can be achieved by solving the Scheduled Service Network Design Problem (SSNDP), one of the classic optimization problems in transportation and logistics. The SSNDP is relevant [3,6,12,13] to carriers in every mode of transportation other than pipeline. Variants of the SSNDP can also inform the planning of transportation operations within a supply chain [7,8]. There has been significant research on the SSNDP [16]. However, nearly all research to date is based on an integer program formulated on a time expanded network. This is in contrast to research on another classical optimization problem relevant to transportation and logistics, the Capacitated Vehicle Routing Problem (CVRP) [46]. There are two modeling strategies for the CVRP that have received significant academic attention, with each having advantages and disadvantages. The first involves modeling vehicle flows on arcs and is often referred to as the compact formulation. With this strategy variables can typically be enumerated a priori, but the resulting integer program has a weak linear programming relaxation and thus can be computationally challenging to solve. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 3–24, 2023. https://doi.org/10.1007/978-3-031-38145-4_1

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The second involves modeling vehicle flows on routes and is often referred to as the extended formulation. With this strategy there are often too many variables for static enumeration to be computationally effective. However, the resulting integer program has a strong linear programming relaxation and thus can be easier to solve. The classical formulation of the SSNDP that is based on a time expanded network can be seen as analogous to the compact formulation of the CVRP. In this work, we present a comprehensive and pedagogical review of three papers [30–32], that focus on a recently proposed new formulation of the SSNDP or one of its variants. This newly proposed formulation can be seen as analogous to the extended formulation of the CVRP as it involves explicitly modeling with consolidations of shipments. Relatedly, this new formulation exhibits some of the same algorithmic advantages and challenges as the extended formulation of the CVRP. It also facilitates modelling operational issues that to date have been challenging to capture with a time expanded network-based formulation. As noted, this paper summarizes the findings of three papers. The first, [31], introduces the overall idea of formulating with consolidations and presents a computational study of its effectiveness. The second, [30], focuses on a variant of the SSNDP in which the physical path for each shipment has already been determined and proves that formulating with consolidations yields a stronger linear programming relaxation in that simplified setting. The third, [32], illustrates the computational effectiveness of modeling with consolidations when transportation costs exhibit a piecewise linear structure. This chapter is organized as follows. Section 2 presents a formal problem description of the SSNDP as well as the classical time expanded network formulation of that problem. Section 2 also discusses operational issues that are difficult to model with such a formulation as well as computational challenges encountered when solving instances of that formulation. Section 3 reviews the newly proposed formulation and draws analogies between the two formulations. Section 3 also identifies operational issues that the new formulation facilitates modeling as well as reasons why it may be easier to solve, computationallyspeaking. Finally, Sect. 4 concludes the paper and provides perspectives on future research.

2

Problem Statement and Time Expanded Network Formulation

In this section we first describe the classical Scheduled Service Network Design Problem (SSNDP). We also provide references to work that has extended the problem to issues that arise in particular operational contexts or transportation modes. The classical Scheduled Service Network Design Problem (SSNDP) presumes a known set of shipments and a terminal network through which each shipment should be routed. It is a deterministic optimization problem wherein the vehicle capacity required by each shipment is presumed known with certainty. Other

Consolidation-Based Modelling for the SSNDP

5

relevant dynamics of the transportation system, such as vehicle transportation times and capacities are also presumed known with certainty. It is a cost minimization problem, as the shipments to be routed, and the prices the carrier charges for doing so, are presumed to have already been determined. The SSNDP is often viewed as a tactical planning problem. It presumes that strategic decisions regarding infrastructure (e.g. terminal locations, vehicle fleet size and mix) have already been made. Conversely, operational complexities such as developing driver schedules that abide by governmental regulations are typically not considered. A common use case for the SSNDP is to develop a transportation plan for a planning horizon that will then be repeated over a longer period of time. For example, to develop a baseline weekly plan indicating where and when shipments and vehicles should move that will be repeated for a season. The SSNDP identifies a path for each shipment in a known set of shipments through a network of known terminals. The SSNDP also prescribes a dispatch time for each terminal to terminal movement on the path for each shipment. Constraining the determination of these dispatch times is the need to respect shipment available and due times and travel times. In some variants, a shipment may be split into smaller shipments, each of which travels on its own path either in space or time. However, in many contexts such splitting is undesirable as it requires extra handling of the freight, which in turn increases the likelihood of damage. In international contexts in which the shipment is a container, such splitting may conflict with documentation requirements. Thus, in this work we presume a single path is chosen for each shipment and the entire shipment must dispatch at the same times on arcs in that path. The SSNDP also determines the number of vehicles that dispatch at a given time for each terminal to terminal movement within the network. Constraining these decisions is the need to ensure that sufficient vehicle capacity is dispatched to carry the shipments to be transported. That said, the SSNDP is an aggregate capacity-type model as it does not capture loading or bin packing-type considerations. It also presumes a homogeneous fleet of vehicles with respect to capacity. There are multiple issues that the classical SSNDP does not model. For example, it does not model the movements of individual vehicles. Nor does it recognize shipments that can not travel in the same vehicle due to their nature (e.g. hazardous chemicals and food). Variants of the SSNDP that are typically studied do not model terminal-level constraints such as limits on the number of simultaneous departures from or arrivals to a terminal. Such constraints may arise in ground transportation wherein departures and arrivals each require a door at the terminal. Nor does the classical SSNDP model terminal capacities with respect to shipments, either in number of size. The SSNDP seeks to minimize total vehicle transportation costs which is typically computed based on the number of vehicles that travel on a move between terminals and the transportation cost associated with that move. The SSNDP also presumes a homogenous fleet of vehicles with respect to these transporta-

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tion costs. Sometimes costs associated with handling freight are included in the objective. However, these are typically of a much smaller magnitude than transportation costs. A description of the SSNDP is provided in the recent survey [16] on Service Network Design. We consider two other variants of the SSNDP in this review for which consolidation-based formulations have proven to be effective. The first is the Service Network Scheduling Problem (SNSP) [30], which considers the same setting but also presumes the path for each shipment has already been determined. Thus, it seeks to identify the shipment and vehicle dispatch times so as to minimize vehicle transportation costs. The SNSP can be viewed as a subproblem of the SSNDP. In the classical SSNDP, transportation costs follow a step function that models constant per-vehicle costs. However, in many practical settings transportation costs are quoted on a per-unit-of-flow basis with the rate decreasing in total flow. In other words, transportation costs exhibit economies of scale. Thus, the second variant we consider is the SSNDP with piecewise linear costs [32]. 2.1

Common Notation

Before presenting individual formulations, we present notation that is common to the two. The physical terminal network is represented by a directed network D = (N, A). The set N models consolidation terminals in the network while the set A models physical transportation moves between those terminals. There is a fixed cost of fi j incurred for each vehicle of capacity ui j that travels on arc (i, j) ∈ A. That the SSNDP presumes a homogeneous fleet of vehicles can be seen in that both fi j and ui j are indexed only by the transportation move. Further, in many practical applications ui j does not depend on the transportation move at all (e.g. ui j = u ∀(i, j) ∈ A). The travel time on arc (i, j) ∈ A is represented by τi j . Note this notation presumes that travel time is independent of departure time. The SSNDP presumes activities are to be planned over a planning horizon that is T periods long. There is a known set of shipments K the carrier is tasked with transporting. Associated with shipment k ∈ K is a terminal where it is to be picked up, ok , and a terminal where it is to be delivered, dk . Pickup of shipment k should occur no earlier than the release date ek , and delivery should occur no later than the due date lk . Also associated with shipment k is its size qk , expressed in the same unit as vehicle capacity. There is a known set of paths Pk from ok to dk for each shipment k ∈ K. Such a set, Pk , may contain all paths in the transportation network from ok to dk . However, in many practical applications there are criteria paths in the set Pk must satisfy. The SSNDP must choose a single path from Pk for each shipment k ∈ K. The SNSP can be formulated by only including in the sets Pk the paths already determined for shipments. As further notation, the set Ki j denotes the set of shipments k such that there exists a path in Pk that contains the arc (i, j). Relatedly, P(i, j)k ⊆ Pk denotes the set of paths in Pk that contain arc (i, j). Finally, given the release and due dates of shipment k as well as travel times, a

Consolidation-Based Modelling for the SSNDP kp

7

kp

time window [αi j , βi j ] can be derived for when k can dispatch on arc (i, j) if it takes path p. 2.2

Time Expanded Network Formulation

Carriers solve the SSNDP to identify shipment and vehicle transportation plans that facilitate shipment consolidation. For two shipments to consolidate in the same vehicle, they must be scheduled to dispatch on the same terminal to terminal movement at the same time. In other words, consolidation requires synchronization of both the paths the two shipments take and the times at which they dispatch on those paths. The classical integer programming formulation of the SSNDP captures this synchronization by modeling the routing of shipments and vehicles on a time expanded network. In such a network, nodes model activities at terminals at different points in time. Arcs model terminal to terminal movements dispatched at different times. As a result, the synchronization of shipments paths and dispatch times is represented by shipments traveling on the same arc. Knapsack-type linking constraints on those arcs ensure sufficient vehicle capacity. Thus, we next present this formulation of the SSNDP, noting that it is used in most papers [16]. This time expanded network is based on a discretization of the planning horizon, T = {1, . . . , T }. As an example, with a planning horizon that represents a six day week, the discretization may involve 144 periods wherein a period represents an hour. This discretization T is used to construct the time expanded network D T = (NT , A T ). The node set NT consists of nodes of the form (i, t), i ∈ N, t ∈ T, that model actions that occur at terminal i during the time period represented by t. There are two sets of arcs in the set A T . Arcs of the form ((i, t), ( j, t )), (i, t), ( j, t ) ∈ NT , i  j, t  = min(t  ∈ τ|t  − t ≥ τi j ) that model traveling from terminal i at time t to arrive at terminal j at time t  are included in the first set. Such arcs are sometimes referred to as travel arcs. Arcs of the form ((i, t), (i, t + 1)), (i, t), (i, t + 1) ∈ NT that model idling at terminal i from period t to period t + 1 are included in the second set. Such arcs are sometimes referred to as holding arcs.  The integer variables yittj , ((i, t), ( j, t )) ∈ A T represent the number of vehicles to be dispatched on arc ((i, t), ( j, t )). For shipment k, the choice of path  p ∈ Pk is indicated by the binary variable v pk . The binary variables xiktt j ,k ∈ K, ((i, t), ( j, t )) ∈ A T denotes whether shipment k takes arc ((i, t), ( j, t )). Given these decision variables, the TEN(D) formulation of the SSNDP is as follows.   fi j yittj zTEN(D) = minimize ((i,t),(j,t  ))∈A T

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subject to  v pk = 1 p ∈Pk



p ∈P(i, j)k

v pk =

 ((i,t),(j,t  ))∈A T



 (t,t  ):((i,t),(j,t  ))∈A T

 xiktt j

xiktt j



−

((j,t  ),(i,t))∈A T



t x kt ji

∀k ∈ K,

(1)

∀(i, j) ∈ A, k ∈ Ki j ,

(2)

⎧ ⎪ 1 (i, t) = (ok , ek ) ⎪ ⎨ ⎪ = −1 (i, t) = (dk , lk ) ⎪ ⎪ ⎪0 otherwise ⎩

∀(i, t) ∈ NT , ∀k ∈ K,

 qk xiktt j

≤

k ∈K  yittj ∈ N v pk ∈ {0, 1}  xiktt j ∈ {0, 1}

 ui j yittj



(3)

∀((i, t), ( j, t )) ∈ A T ,

(4)

∀((i, t), ( j, t )) ∈ A T ,

(5)

∀k ∈ K, p ∈ P(i, j)k ,

(6)



∀((i, t), ( j, t )) ∈ A T , k ∈ K.

(7)

The objective function seeks to minimize the sum of vehicle transportation costs on arcs the network. Constraints (1) ensure one path is selected for each shipment. Constraints (2) ensure that each transportation move in a selected path is scheduled. While constraints (1) ensure a physical path is chosen for each shipment, the flow balance constraints (3) expressed on the time expanded network ensure the dispatch times for moves in that path agree with release dates, due dates, and travel times. Constraints (4) ensure sufficient vehicle capacity to transport shipments on arcs in the time-expanded network. Finally, constraints (5)–(7) define the decision variables and their domains. We note that when Pk contains all feasible paths between ok and dk , the resulting formulation is equivalent to a formulation which does not explicitly model at the path level. Researchers have extended both the scope of decision-making considered by the SSNDP as well as the system dynamics it considers. A classic and important example is asset or resource management [1,2,17,21,29,35,43,45], which typically refers to the need to move a vehicle empty to position it for a future transportation move. Finally, we note that formulating the TEN(D) necessitates choosing a discretization of time. To the best of our knowledge, the issue of how that choice impacts the quality of the plan derived from solving the resulting TEN(D) has only been considered in [10]. 2.3

Modeling Limitations

While the SSNDP is a rich mathematical model with application to many freight transportation contexts, there are many operational realities that are not easily captured in the formulation just presented. We will next discuss three such

Consolidation-Based Modelling for the SSNDP

9

realities: (1) loading constraints, (2) piecewise-linear costs, and, (3) proportional emissions accounting. Loading Constraints: Fundamentally, the TEN(D) treats capacity in aggregate and thus can underestimate capacity needs. To illustrate the issue, consider three shipments k, k , k  in which qk = qk  = qk  = 2 and vehicle capacity ui j = 3. In other words, each shipment requires two thirds of vehicle capacity. Focusing on constraint (4), we      = xikj tt = xikj tt = 1 for some (t, t ) the left-hand side of the see that with xiktt j  constraint would evaluate to 6, requiring yittj ≥ 2. Yet no two of k, k  and k  can fit within a single vehicle. Recognizing this “bin packing-type” consideration implies that three vehicles are actually needed. Alternately, suppose qk  = 1, but k  represents toxic chemicals while k and k  represent food items. In this case, the aggregate capacity requirement is five and thus a solution to TEN(D) would prescribe two vehicles (again presuming ui j = 3). Furthermore, from a capacity perspective, k  can travel in the same vehicle as either k or k , while k and k  can not travel in the same vehicle together. However, due to the nature of the products, this is not possible given the nature of the shipments, and three vehicles are again needed. Finally, the TEN(D) does not capture the need to load a set of shipments into a vehicle and that their spatial dimensions may require more vehicles than indicated by adding their individual capacity needs. A practical example of such a consideration is when transporting assembled furniture, wherein the nonrectangular shapes of items can make it difficult to fully utilize vehicle capacity in terms of weight or cubic volume. While such considerations have been considered in the literature on the Vehicle Routing Problem [42], it has not yet been done for the SSNDP. To summarize, adapting the TEN(D) to accurately estimate vehicle capacity needs in light of the issues just raised is not trivial and has not yet been done. Piecewise Linear Costs: The SSNDP presumes the carrier uses its own vehicles to transport shipments and thus costs are incurred on a per-vehicle basis. However, in many contexts and situations in practice the carrier may resort to outsourcing the transportation of one or more shipments to a third party. This third party may in turn charge the carrier on a per unit of volume transported basis (e.g. per kilogram or per pound). In addition, these prices may exhibit economies of scale. Namely, the third party charges a price per unit of volume that decreases as the total volume it is asked to transport increases. Typically, such cost structures are modeled with piecewise linear cost functions (see eg. [4,18,36]). To be precise, consider a piecewise-linear cost function gi j (wi j ) that depends on the load wi j on arc (i, j) ∈ A. Such functions have received extensive study [4,19,22,24,26], with [25] presenting a review. It is typically presumed that this function is determined by a finite set of segments Si j = {1, ..., |Si j |}, in which each 1 s segment s ∈ Si j is defined by a lower and upper bound [bis− j , bi j ] on the load wi j , s s s s 1 s an intercept fi j and a slope ci j , such that gi j (wi j ) = fi j + ci j wi j if wi j ∈ [bis− j , bi j ].

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M. Hewitt

Applying the classical approach to modeling such a cost function in the con text of the TEN(D) is as follows. First, the integer decision variable yittj that  represents vehicle usage is replaced with a binary variable yistt j that represents 1 s whether the load on ((i, t), ( j, t )) falls within [bis− j , bi j ], s ∈ Si j Second, the contin stt + uous variable zi j ∈  , models the load on this arc when it falls within segment s ∈ Si j . Then, the objective of the TEN(D)is changed to the following.    s stt  ( fisj yistt (8) zTEN(D) = minimize j + ci j zi j ) ((i,t),(j,t  ))∈A T s ∈Si j

This objective aggregates the fixed and variable costs of chosen segments s ∈ Si j for each arc ((i, t), ( j, t )) ∈ A T . Ensuring the appropriate segments are chosen given the flow of shipments in the time-expanded network is accomplished by adding the following constraints to the TEN(D), while both the capacity and coupling constraints (4) and variable definition constraints (5) are removed. 





k ∈Ki j

qk xiktt j = 

s ∈Si j 

zistt j



1 stt stt s stt bis− j yi j ≤ zi j ≤ bi j yi j   yistt j ≤ 1

∀((i, t), ( j, t )) ∈ A T , 

(9)

∀((i, t), ( j, t )) ∈ A T , s ∈ Si j ,

(10)

∀((i, t), ( j, t )) ∈ A T ,

(11)

∀((i, t), ( j, t )) ∈ A T , s ∈ Si j

(12)

s ∈Si j 

yistt j ∈ {0, 1} stt 

zi j

≥0



∀((i, t), ( j, t )) ∈ A T , s ∈ Si j

(13)

While we see that piecewise linear cost functions can be easy to model, embedding such functions in the TEN(D) requires additional variables and constraints. This can in turn yield instances that are computationally harder to solve. Proportional Emissions Accounting: Many shippers are now expecting transportation carriers to report the carbon emissions associated with transporting their shipments as a first step towards attempting to reduce the environmental impact of their supply chains. Such reporting is often done based on frameworks like the Global Logistics Emissions Council (GLEC) framework [23]. That framework computes the emissions for a shipment on a vehicle move by allocating the emissions associated with the vehicle move in proportion to that shipment’s portion of the total volume transported by that vehicle on that move. With such an accounting, for the same vehicle transportation move, a shipment that travels on its own in a vehicle is allocated greater emissions than one that travels with many other shipments. However, it is likely that shippers will go beyond requiring carriers to report the emissions associated with transporting their shipments. In particular, that shippers will consider providing a carrier a budget for the emissions associated

Consolidation-Based Modelling for the SSNDP

11

with their shipments as a mechanism for reducing the overall environmental impact of their supply chains. Yet, how to model such a budget in the context of the TEN(D) given the proportional accounting just described is not obvious. To illustrate a potential, approximate, approach, presume i j represents the emissions associated with a vehicle traveling from terminal i to terminal j, assuming emissions are not departure time dependent. Also, assume that associated with shipment k is a budget Υk on the total emissions realized from its transportation from ok to dk . One could formulate the following constraint.  ((i,t),(j,t  ))∈A T

i j 

qk xiktt j k ∈K



qk xiktt j



≤ Υk

(14)

However, such a constraint would underestimate the emissions associated with a shipment when there is more than volume than one vehicle can transport moving from i to j at time t. In addition, embedding such constraints in the TEN(D) would leave a non-linear program that is likely quite difficult to solve. 2.4

Algorithmic Challenges

Significant research has been done on the development of algorithms for solving time expanded network-based formulations of the SSNDP like the TEN(D). Many papers have focused on proposing exact methods [9,15,28,38]. Many of these methods focus on strengthening the integer programming formulation of the problem [15,34] while others [9,28,38] focus on managing the size of the time expanded network. Others have proposed heuristics [14]. Some propose metaheuristics [27,40], while others propose matheuristics [11,21,33]. Even in light of these advances, solving large-scale instances of the SSNDP formulated as a TEN(D) presents multiple computational challenges. We will next discuss these challenges. A first challenge is that the fine level of discretization needed to find highquality solutions by solving an instance of the TEN(D) can lead to extremely large time expanded networks. As both the x and y variables in the TEN(D) have domains that depend on the size of the underlying time expanded network, this in turn leads to large integer programs that can be difficult to solve. This challenge is somewhat mitigated by the Dynamic Discretization Discovery (DDD) framework presented in [9] and further studied in [28,38]. The key idea proposed in this framework is to generate the time expanded network on which the TEN(D) is formulated in an iterative, as opposed to in a static, a priori manner. Computational results [9,28,38] have shown that algorithms based on the proposed framework produce small time expanded networks on which instances of the TEN(D) can be formulated and solved to yield provably high-quality solutions. However, DDD is challenging to implement and unable to solve some instances derived from real-world operations [9,28]. To illustrate the second challenge, we note that the arc set A T can contain multiple arcs that represent the same physical transportation move only dispatched at different times. This duplication can cause the network to contain

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M. Hewitt

multiple copies of a physical path for a shipment that differ only in the dispatch times of the physical moves in that path. As the SSNDP does not typically consider the timing of activities in its objectives, these copies are equivalent.

Fig. 1. Example network with two commodities: k,k’. qk = qk  = 1, ui j = 2, fi j = 100 ∀(i, j)

We illustrate this phenomenon in Fig. 1 wherein there are two shipments to be routed, with shipment k originating at node (1, 1) and shipment k  originating at node (2, 2). However, both are destined for node (3, 6). Both shipments require one unit of vehicle capacity whereas vehicle capacity on all arcs is two. In this network, shipment k can take ten different paths that consist of the physical moves (1, 2), (2, 3). Similarly, shipment k  can take four paths that consist of the physical move (2, 3). These copies of shipment paths can in turn lead the TEN(D) formulated on this network to be highly symmetric, meaning there are multiple solutions that have the same objective function value. These multiple solutions to the integer program in turn lead to multiple solutions to its linear programming relaxation, which can render a branch and bound-based [39] solution method ineffective. We illustrate the complications such symmetry can cause a branch and bound-based algorithm in Figs. 2 and 3. In Fig. 2 we illustrate a solution to the linear relaxation of the integer program associated with the example in Fig. 1.   Let yittj L P represent the value of the variable yittj in a solution to the linear programming relaxation of TEN(D). It is well-understood that when fi j > 0 a    solution to the linear relaxation of TEN(D) will have yittj L P = ( k ∈K qk xiktt j )/ui j . 12 L P = .5 and total In our example and considering arc ((1, 2), (1, 2)), we have y12 12 L P 23 L P costs of 100y12 +100y23 = 150. Given the nature of branch and bound-based algorithms, it is likely that the algorithm would proceed by branching on the 12 12 , with one branch enforcing the constraint y12 ≤ 0. variable y12 However, a likely solution to the linear programming relaxation after enforcing such a branching constraint is illustrated in Fig. 3. We see that while the

Consolidation-Based Modelling for the SSNDP

13

Fig. 2. Initial solution to linear programming relaxation

12 constraint y12 ≤ 0 is observed, the objective function value of the solution to the linear programming relaxation is still 150 as the solution to the LP simply shifts 12 ≤ 0 did not advance the dispatch time of shipment k. In effect, branching on y12 the solution process. Research has been done to reduce symmetry in mixed integer programs in general [37,41]. However, we are unaware of any research that focuses on symmetries inherent to formulating on a time expanded network.

12 ≤ 0. Fig. 3. Solution to linear programming relaxation after branching on y12

A third challenge is due to the weak linear programming relaxations that result from modeling capacity needs with the knapsack-type linking constraints 12 L P = .5 and then (4). This is also illustrated in Figs. 2 and 3 where we have y12 23 L P = .5 As most integer programming solvers rely on bounding techniques y12 that involve solving linear programming relaxations to establish the quality of a solution, weak bounds can lengthen the time needed to establish that a solution is optimal to within a given tolerance. This challenge is somewhat mitigated by

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valid inequalities developed for network flow models such as Flow cover inequalities [39]. However, even with such inequalities the TEN(D) is notorious for having a weak linear programming relaxation.

3

A Consolidation-Based Formulation of the SSNDP

To date, the TEN(D) represents the primary paradigm for formulating the SSNDP as an integer program. However, one can look to the Capacitated Vehicle Routing Problem (CVRP) [46] as an example of an optimization problem that is fundamental to planning logistics operations wherein there are multiple formulations. Namely, the literature on the CVRP generally considers one of two formulations. The first models the movement of vehicles on arcs between customers and thus is often thought of as arc-based. It relies on constraints seen in the Traveling Salesman Problem to ensure those movements form a route. It relies on knapsack-type constraints to ensure the total volume carried by each vehicle does not exceed its capacity. This formulation is often referred to as the compact formulation as its variables can typically be enumerated with little computational difficulty. The second formulation models the movement of vehicles on routes that visit sets of customers. Instead of constraints enforcing vehicle movements that constitute routes and that vehicle capacity is observed, only routes that satisfy vehicle capacity constraints are considered. This route-based formulation is typically much stronger than the arc-based formulation. This is primarily because the need for routes that do not violate vehicle capacity is embedded in the variables considered by the formulation as opposed to enforced via constraints. This second formulation is often referred to as the extended formulation as there are often an exponential number of route-based variables that must be generated dynamically using a method like column generation [5,20]. Turning back to the SSNDP, the problem we are considering, one can draw a parallel between the compact formulation of the CVRP and the TEN(D) as both rely on knapsack-type constraints to ensure sufficient vehicle capacity is allocated. The consolidation-based formulation proposed in [31] that we next review can be seen as an analog of the extended formulation as variables associated with consolidations of shipments encode capacity needs. 3.1

Integer Programming Formulation

We next present a formulation of the SSNDP wherein vehicle capacity needs are modeled by variables that encode consolidations of shipments, wherein a consolidation is defined as a set of shipments that dispatch on the same physical move at the same time. There are two significant differences between this formulation and the TEN(D). The first is that it is defined on the network (N, A) instead of the time expanded network D T . This removes many of the symmetry issues present in the TEN(D). The second is that it assumes an a priori enumeration of consolidations on each arc (i, j) ∈ A. As such, instead of relying on knapsack-type constraints to compute vehicle needs, vehicle needs are coefficients associated with consolidation variables.

Consolidation-Based Modelling for the SSNDP

15

To present this formulation we let Ωi j define the set of possible shipment consolidations on arc (i, j). An element ω ∈ Ωi j is a subset of shipments (i.e. ω ⊆ K). To be explicit, the formulation presumes consolidations that are singletons are present in Ωi j . Namely, if there is a path for shipment k that involves the physical move (i, j), then Ωi j contains the consolidation w = {k}. For ω ∈ Ωi j , (i, j) ∈ A, we k denote whether shipment k ∈ K is contained in consolidation ω. Regardlet φω ing vehicle capacity, we let sω define the number of vehicles needed to transport consolidation ω ∈ Ωi j . Reproducing the capacity needs modelled by TEN(D)  k ∈ω qk can be accomplished by setting sω =  ui j . The binary decision variable δω indicates whether the consolidation ω ∈ Ωi j is chosen. The binary variables v pk ∈ {0, 1}, p ∈ Pk , k ∈ K serve the same purpose as in the TEN(D). Unlike the TEN(D), the time at which shipment k dispatches on arc (i, j) ∈ A is modeled with the decision variable γikj . The consolidation-based formulation presented in [31], and labeled as CONS(D), is as follows.   fi j sω δω zCONS(D) = minimize (i, j)∈A

subject to  v pk = 1

ω ∈Ωi j

∀k ∈ K,

(15)

∀(i, j) ∈ A, k ∈ Ki j ,

(16)

∀(i, j) ∈ A, k, k  ∈ Ki j ,

(17)

∀(i, j) ∈ A, k ∈ Ki j ,

(18)

∀(i, j) ∈ A, k ∈ Ki j ,

(19)

∀k ∈ K,

(20)

∀k ∈ K,

(21)

∀ j ∈ N, k ∈ K,

(22)

v pk ∈ {0, 1}

∀k ∈ K, p ∈ P(i, j)k ,

(23)

γikj

≥0

∀k ∈ K, (i, j) ∈ A, ,

(24)

δω ∈ {0, 1}

∀ω ∈ Ωi j , (i, j) ∈ A,

(25)

p ∈Pk



ω ∈Ωi j

k φω δω = 

 p ∈P(i, j)k





ω ∈Ωi j



k k φω φω δω )

kp

p ∈P(i, j)k

γikj ≤





γikj − γikj ≤ Mikk j (1 −

v pk

αi j v pk ≤ γikj 

p ∈P(i, j)k

(o k , j)∈A



(i,dk )∈A



(i, j)∈A

kp

βi j v pk

γok k j ≥ ek

k (γid + τidk ( k

(γikj + τi j (

 p ∈P(i,dk )k



p ∈P(i, j)k

v pk )) ≤ lk

v pk )) ≤

 (j,i)∈A

γ kji

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Constraints (15) are duplicates of (1) and repeated for completeness. Constraints (16) ensure that given the path chosen for a shipment, a consolidation is chosen for each arc in that path. Constraints (17) ensure that when a consolidation is chosen, all pairs of shipments in that consolidation dispatch at the same time. While constraints (17) are big-M type constraints, which often lead to weak linear programming relaxations, careful analysis regarding when com modities k, k  can dispatch on the physical move (i, j) can lead to values of Mikk j that mitigate this issue. Constraints (18) and (19) ensure that the dispatch time for a shipment on an arc falls within the time window induced by the path chosen for that shipment. In addition, constraints (19) ensure that if a path containing an arc is not chosen for a shipment, then the dispatch time for that shipment on that arc is zero. Constraints (20) and (21) ensure shipment release and due dates are observed. Constraints (22) ensure that the dispatch variables associated with arcs that are sequential in any path for a shipment agree with transportation times. Finally, constraints (23), (24), and (25) define the decision variables and their domains. A consolidation-based formulation of the SNSP is presented in [30], in which the sets Pk contain just a single path. This enables the derivation of narrower  kp kp time windows [αi j , βi j ], smaller values Mikk j , and tighter linear programming relaxations. 3.2

Correspondence Between the Two Formulations

TEN(D) and CONS(D) represent two integer programming formulations of the same problem, the SSNDP. Fundamentally, the SSNDP seeks to choose a path for each shipment, determine dispatch times for each physical move in the path chosen for each shipment, ensure those dispatch times agree with travel times and the release and due times for that shipment, and compute the corresponding vehicle needs. As already noted, it does so with the goal of minimizing vehicle transportation costs. We summarize in Table 1 what aspects of each of the two formulations, TEN(D) and CONS(D), capture these constraints. Table 1. Correspondence between two formulations Logical constraint

TEN(D) constraints CONS(D) constraints

A path for each shipment

(1)

(15)

Feasible dispatch times for each physical move on path chosen for each shipment

(2)

(18), (19)

Dispatch times for shipment on its chosen (3) path that agree with travel, release, and due times

(20), (21), (22)

Accurately compute vehicle needs

(16), (11), (17)

(4)

Regarding the objective functions of TEN(D) and CONS(D), we note that the objective function of TEN(D) aggregates timed vehicle dispatches on a phys-

Consolidation-Based Modelling for the SSNDP

17

ical move whereas the objective function of CONS(D) deals directly with aggregated vehicle dispatches. We will next discuss how the CONS(D) formulation of the SSNDP facilitates modeling some operational realities, while making others more challenging to represent. 3.3

Modeling Capabilities

We first discuss how the CONS(D) facilitates modeling the operational issues discussed in Sect. 2.3. Loading Constraints: Recall that CONS(D) captures vehicle needs for consolidation ω with the coefficients sω . Thus, the loading constraints discussed in Sect. 2.3 can be captured with data, potentially by solving an optimization problem to generate the values of those coefficients. Returning to the example discussed in that section, suppose consolidation ω = {k, k , k  } with qk = qk  = qk  = 2 and ui j = 3. The solution of a small bin-packing problem yields that sω = 3 as no two shipments can fit in a single vehicle. We note that when such bin packingtype capacity usage is recognized there is no need to consider consolidations that occupy more than a single vehicle. More complicated loading constraints that explicitly recognize the spatial dimensions of shipments can also be captured with the coefficients sω, albeit potentially at the expense of solving more complicated optimization problems to determine the values of those coefficients. Similarly, shipments that can not be transported in the same vehicle can be modelled by simply not considering consolidations that contain those shipments. Recall the example where qk  = 1, but k  represents toxic chemicals while k and k  represent food items. This can be modelled by simply not enumerating consolidations that contain k  and either k or k  . Piecewise-Linear Costs: Recall our premise that the carrier may use a third party to transport one or more shipments and that third party will charge a per unit of volume price that decreases as the total volume it is asked to transport increases. To illustrate how such cost functions can be modelled with CONS(D), we suppose a transportation cost function gi j (xi j ) on an arc (i, j) ∈ A that depends on the transported quantity xi j on this arc. Such functions are often presumed to have certain properties. For example, [25] presume the cost function gi j is lower semi-continuous, non-decreasing, and such that gi j (0) = 0. Like vehicle capacity needs, with CONS(D), we will capture costs prescribed by this function with coefficients. represent consolidations of shipments that are transNamely, we let Ωiout j ported by a third party. Then, for all (i, j) ∈ A, ω ∈ Ωiout j , we define the cost  ω coefficient κi j = gi j ( k ∈ω qk ) to capture the costs incurred by having the third party transport all shipments in ω together on arc (i, j). Then, a piecewise linear cost function can be integrated into the formulation by including in   CONS(D) the objective function the expression (i, j)∈A ω ∈Ωiouj t κiωj δω . Proportional Emissions Accounting: Recall our premise that the emissions associated with each shipment transported by a vehicle is determined by allocating

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the emissions from the vehicle move proportionally. Like the two previously identified modeling issues, such an accounting can be captured in CONS(D) with data. Recall again that we presume i j , (i, j) ∈ A represents the emissions associated with a vehicle transporting shipments from terminal i to terminal j. Also presume that only consolidations such that sω = 1 are considered, as is sufficient when bin packing-type capacity usage is recognized. We then com pute iωk j = i j (qk / k  ∈ω :k  k qk ) to represent the proportioned allocation of the emissions i j to shipment k when consolidation ω is chosen. A budget on the emissions associated with transporting shipment k can then be captured with   the constraint (i, j)∈A ω ∈Ωi j iωk j δω ≤ Υk . 3.4

Algorithmic Advantages and Disadvantages for the SSNDP

[31] compare the computational complexity associated with solving the two formulations of the SSNDP. To do so, we present results from a computational analysis involving a set of randomly generated instances based on a portion of the network of a United States-based LTL carrier. Specifically, a portion of the network consisting of the states of Alabama, Florida, Georgia, and South Carolina. Together, these states contain 25 terminals (e.g. |N | = 25) and there are 530 physical moves between terminals in these states (e.g. |A| = 530) the carrier considers executing. Parameter values regarding transportation costs, capacities, and travel times were based on data provided by the carrier. [31] generated Instances that vary in two parameters. The first is the number of shipments, or, the size of the set K, for which we consider the values 100,150,200,250, and 300. Historical data regarding freight transported was used to randomly generate shipments. The second is the number of potential paths for each shipment (i.e. |Pk |), for which we consider the values 5,6,7,8,9, and 10. To construct the set Pk for a given number of paths μ and shipment k all paths from ok to dk were enumerated to identify a set of candidate paths. Paths from this candidate set for which the total time required to travel that path exceeded the time available to deliver the shipment were then removed. We then included in Pk the μ shortest paths with respect to travel time. We generated five instances for each combination of number of shipments and number of paths per shipment, yielding a set of 150 instances. [31] used a computer equipped with 64 Intel Xeon Gold 6130 CPU processors, with each operating at 2.10 GHz, for all experiments. At the time of experimentation, the computer ran the Ubuntu distribution of the Linux operating system. Instances of both formulations were solved with CPLEX 12.10 [44]. All code to create and solve instances was implemented in Python 3.7 [47] and run for two hours, or, 7,200 s. The solver was configured to use a single thread and solve to a tolerance of 1%. No other parameters of CPLEX were changed from their default values. We next review and summarize the results of these experiments presented in [31]. We first observe that the solver was able to solve more instances of the CONS(D) formulation (85) than the TEN(D) formulation (67). We note that all 30 instances of CONS(D) with 100 shipments were solved while only 3 of the

Consolidation-Based Modelling for the SSNDP

19

30 instances with 300 shipments were solved. However, 0 of the 30 instances of the TEN(D) formulation with 300 shipments were solved. Considering instances wherein both formulations were solved, instances of CONS(D) were solved, on average, in 257.56 s. Of those instances, the TEN(D) were solved in 996.54 s. These results establish that instances of CONS(D) can be easier to solve than those of TEN(D). Thus, we next seek to understand to what extent CONS(D) mitigates the challenges associated with solving instances of TEN(D) that were identified in Sect. 2.4. Instance Size: CONS(D) models actions on the physical network. TEN(D) instead models actions on a time expanded network. Such a network is constructed by creating “timed copies” of the physical network to represent actions on the physical network occurring at different points in time. As both the variables and constraints defining TEN(D) are defined over this time expanded network, instances of the TEN(D) can be large and thus more difficult to solve. While CONS(D) models on the physical network, it does involve a variable for each possible consolidation, potentially causing instances of the CONS(D) to be quite large. We report the average size of instances of the two formulations in Table 2. Table 2. Average instance sizes Formulation Number of Constraints Number of Variables CONS(D)

12,302.91

55,567.19

TEN(D)

23,965.99

49,666.22

We see that while instances of CONS(D) have slightly more variables than instances of TEN(D) they have nearly half as many constraints. We note that the time required to solve the root node linear programming relaxations of each formulation is roughly the same. Thus, it is not immediately clear how the differences in size of instances of these formulations impacts their solvability. Symmetry: As discussed in Sect. 2.4, the nature of a time expanded network can lead to instances of TEN(D) that suffer from symmetry which can in turn slow down the branching process used in a branch and bound-based solution method. Thus, to compare the solvability of the two formulations on this dimension we report the number of nodes in the branch and bound tree at solver termination. Specifically, upon terminating the solution of instances of CONS(D), there were on average 4,594.04 nodes in the branch and bound tree. For instances of TEN(D) there were 7,766.08 nodes. This data suggests that greater symmetry in instances of TEN(D) may lead to those instances being harder to solve. Capacity Constraints: As discussed in Sect. 2.4, the knapsack-type linking constraints (4) can yield linear programming relaxations of instances of TEN(D)

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M. Hewitt

that provide very weak bounds on objective function values of optimal solutions. CONS(D), on the other hand, encodes capacity needs in variable coefficients and thus its linear programming relaxation is likely to yield stronger bounds. To study this issue, the linear programming relaxations of the CONS(D) and TEN(D) formulations were solved for each instance. The objective function values of the optimal solutions to these linear programming relaxations L PR L PR and zTEN . Given these values, we consider the gap are denoted by zCONS (D) (D) L PR L PR L PR (zCONS(D) − zTEN(D) )/zCONS(D) and note that over all 150 instances the average of this gap is 22.60%, suggesting that CONS(D) is a significantly stronger formulation than TEN(D) . This stronger bound may also explain why solving the CONS(D) formulation requires a smaller branch and bound tree. These results suggest that instances of CONS(D) are easier to solve than those of TEN(D) overall. However, as the number of shipments in an instance increases, and thus the potential number of consolidations increases, the enumerative nature of CONS(D) renders the instances difficult to generate and solve. This suggests the need for a solution method wherein consolidations are generated dynamically. 3.5

Consolidation-Based Formulations for Two Variants of the SSNDP

In this section, we review the benefits of consolidation-based formulations for two variants of the SSNDP. We first consider the Service Network Scheduling Problem (SNSP) [30]. We recall that the SNSP is effectively the SSNDP, albeit with the physical path already identified for each shipment. Given such a path p¯k , a consolidation based formulation of the SNSP can be formulated as in the CONS(D), only with Pk = { p¯k }. Similarly, a time-expanded network formulation of the problem can be formulated as in the TEN(D). However, [30] also adapt network pruning rules from [9] to remove a significant number of arcs from A T in an a priori manner. [30] prove that the CONS(D) formulation of the SNSP has a stronger linear programming relaxation than the TEN(D) formulation, even after pruning. With instances derived from the same network as those used in [31], albeit with up to 750 shipments, [30] also compare the CONS(D) and TEN(D) formulations computationally. In comparing the gap between the objective function values of these linear programming relaxations [30] show that the CONS(D) formulation is 6.10% stronger. [30] also report that 89.29% of instances of the CONS(D) formulation could be solved whereas only 44.46% of the instances of the TEN(D) formulation, even after network pruning. These theoretical and computational results provide strong evidence that a consolidation-based formulation is superior to a time expanded network-based formulation for the SNSP. The second variant we consider is the SSNDP with piecewise linear costs [32]. As noted, considering such a cost structure with a consolidation-based  formulation requires computing the cost coefficient κiωj = gi j ( k ∈ω qk ) in an a priori manner and using an objective function that minimizes the expression

Consolidation-Based Modelling for the SSNDP



21



κiωj δω . No additional variables or constraints need be added to the CONS(D) formulation presented for the SSNDP. This is in contrast to representing a function g(·) in a TEN(D) formulation, in which both binary and continuous variables need to be added to the formulation. Additional constraints need to be added as well. The computational complexity associated with solving the two formulations is studied in [32]. They consider piecewise linear cost functions that are based on a given vehicle capacity of Q that is divided into M segments of identical lengths. They consider three sets of instances, each based on a different numbers of segments, M = 3, 4, or 5. [32] report computational results on instances derived from the same network as those used in [31], with up to 200 shipments. [32] observe that every instance of the consolidation-based formulation could be solved within a two hour time limit, with an average time to termination of 406.66 s. However, no instances of the time expanded network-based formulation could be solved within that same time limit and the average optimality gap reported at termination was 13.56%. These computational results provide strong evidence that a consolidation-based formulation is superior to a time expanded network-based formulation for the SSNDP with piecewise linear costs. (i, j)∈A

4

t ω ∈Ωiou j

Conclusions and Perspectives

In this work, we reviewed a new modeling approach for an optimization problem that is relevant to the planning of many freight transportation systems, the Scheduled Service Network Design Problem (SSNDP). This modeling approach, which involves explicitly modeling with consolidations of shipments, can be seen as an analog of extended, route-based formulations for the Capacitated Vehicle Routing Problem (CVRP). This approach is in contrast to the classical approach, which relies on a time expanded network, and can be seen as an analog to compact, arc-based formulations of the CVRP. We reviewed results regarding the use of this new modeling approach on three variants of the SSNDP and found that in each case the newly proposed modeling approach is superior to the classical approach. We discussed multiple operational issues that are easier to model with this approach than the classical approach. We also identified operational issues that are easier to model with the classical approach. We also provided evidence that suggests the enumerative nature of this new modeling approach may not scale, computationally-speaking, to large-scale instances. Thus, this new modeling approach may require further algorithmic research. The CVRP and SSNDP are two of the fundamental optimization problems that can assist in the planning of transportation and logistics operations. Both compact and extended formulations of the CVRP have received significant academic attention over the years. In contrast, all research related to the SSNDP that we are aware of has focused on the classical, time expanded network formulation. We believe the formulation discussed in this paper deserves further attention, both because of what it facilitates modelling and its potential for computational tractability.

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37. Margot, F.: Symmetry in integer linear programming. In: J¨ unger, M., et al. (eds.) 50 Years of Integer Programming 1958-2008, pp. 647–686. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-68279-0 17 38. Marshall, L., Boland, N., Savelsbergh, M., Hewitt, M.: Interval-based dynamic discretization discovery for solving the continuous-time service network design problem. Transp. Sci. 55, 29–51 (2020) 39. Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization, vol. 18. Wiley, New York (1988) 40. Pederson, M.B., Crainic, T.G., Madsen, O.B.: Models and tabu search metaheuristics for service network design with asset-balance requirements. Transp. Sci. 43(2), 127–266 (2009) 41. Pfetsch, M.E., Rehn, T.: A computational comparison of symmetry handling methods for mixed integer programs. Math. Program. Comput. 11(1), 37–93 (2019) 42. Pollaris, H., Braekers, K., Caris, A., Janssens, G.K., Limbourg, S.: Vehicle routing problems with loading constraints: state-of-the-art and future directions. OR Spectr. 37, 297–330 (2015) 43. Scherr, Y.O., Saavedra, B.A.N., Hewitt, M., Mattfeld, D.C.: Service network design with mixed autonomous fleets. Transp. Res. Part E Logist. Transp. Rev. 124, 40–55 (2019) 44. ILOG CPLEX Optimization Studio: Users manual-version 12 release 6 (2013) 45. Teypaz, N., Schrenk, S., Cung, V.D.: A decomposition scheme for large-scale service network design with asset management. Transp. Res. Part E Logist. Transp. Rev. 46(1), 156–170 (2010) 46. Toth, P., Vigo, D.: Vehicle Routing: Problems, Methods, and Applications. SIAM (2014) 47. VanRossum, G., Drake, F.L.: The python language reference. Python Software Foundation Amsterdam, Netherlands (2010)

Supply Chain Management

Evaluation of Hydrogen Supply Options for Sustainable Aviation Karen Ohmstede1(B) , Christian Thies2 , Alexander Barke1 , and Thomas S. Spengler1 1 Carl-Friedrich-Gauß-Faculty, TU Braunschweig, Mühlenpfordtstraße 23, 38106

Braunschweig, Germany {karen.ohmstede,a.barke,t.spengler}@tu-braunschweig.de 2 Resilient and Sustainable Operations and Supply Chain Management Group, TU Hamburg, Am Schwarzenberg-Campus 4, 21073 Hamburg, Germany [email protected]

Abstract. From an environmental perspective, green hydrogen is a promising alternative energy carrier for short-to middle-range flights. Furthermore, hydrogen produced from renewable energy releases no carbon dioxide emissions during production and use. Therefore, hydrogen is a potential solution for reducing aviationrelated emissions. Besides, the economic competitiveness of hydrogen against conventional fuels, mainly influenced by the hydrogen supply chain design, will be a key determinant for future hydrogen deployment. The supply chain consists of production, compression, transportation, and liquefaction, but these components’ exact order, sizing, and location are still insecure. Different transport options exist, which are associated with various economic impacts during their purchase and use, as well as various supply chain configurations result in different overall expenses. We analyze demand and distance scenarios using an expense-oriented economic evaluation with CAPEX and OPEX to determine the best transport configuration. The total expenses of hydrogen are highly influenced by the expenses caused by energy and transport volume. Here, pipeline transportation is a promising option, as well as liquid hydrogen truck transportation in cryogenic tanks. It turns out that distance and demand for hydrogen strongly influence the choice of transportation.

1 Introduction The aviation industry has set ambitious goals to reduce its environmental impacts. Passenger transport via airplanes accounted for 2.4% of the overall anthropogenic greenhouse gas emissions in 2018 (Lee et al., 2021). In the same year, passenger air travel alone was responsible for 766 million tons of carbon dioxide (CO2 ) emissions (Graver et al., 2020). Compared to other modes of transport, this is a comparatively small amount, but emissions at higher altitudes cause three times higher radiative forcing (Jungbluth and Meili, 2018; Kords, 2022). Due to the COVID-19 pandemic, air traffic and related emissions declined in 2020 and 2021. However, the annual number of passengers has returned to a pre-COVID level of nearly 3.5 billion (Burgueño Salas, 2022). A current study from Boeing predicts an annual growth rate of flights by 4% for the next 20 years, making the achievement of the main emission reduction targets of the Paris © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 27–42, 2023. https://doi.org/10.1007/978-3-031-38145-4_2

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agreement and the Flightpath 2050 ambitious (Boeing, 2022). The Paris agreement aims to limit global warming to the temperature of 1.5 °C, related to the pre-industrial level and sets boundaries for a CO2 budget of 500 billion tons of CO2 by 2100 (Rogelj et al., 2018). The Flightpath 2050 aims to reinforce the aviation industry’s competitiveness with ambitious emission reduction goals. By 2050, the CO2 emissions need to be reduced by 75% per revenue passenger kilometer (RPK) and nitrogen oxide (NOx ) emissions by 90% per RPK, while commercial flight numbers are forecast to rise to 25 million (European Commission et al., 2011). To achieve these ambitious reduction goals, radical transitions of the propulsion concepts and the avoidance of fossil energy carriers are required (Barke et al., 2022). Sustainable aviation fuels (SAF) are the only suitable option known so far for long-range flights and can be used with little modifications of the aircraft engine. However, they still cause emissions during fuel combustion, which are compensated by CO2 usage during fuel production (Farokhi, 2019). For short and middle-range distances, several options are conceivable. In this context, battery electric aircraft might only be a suitable option for short-range flights due to the limitations in the distance related to the battery weight (Crittenden, 2020). Hydrogen is a promising option because it combines the advantages of SAFs and battery electric propulsion with high specific energy and nearly no greenhouse gas emission during its lifecycle (Ponater et al., 2003). Two basic alternatives for pure hydrogen usage in an aircraft are possible: Hydrogen as an energy carrier for fuel cells to produce electricity and run an electric motor or hydrogen as a propellant directly burned in a modified jet engine (Verstraete, 2008). Here, liquid hydrogen (LH2 ) is the only suitable option for hydrogen as a fuel for aviation. Due to the limited space inside the aircraft, the hydrogen density needs to be as high as possible (Pornet and Isikveren, 2015). Here, the most important aspect for an airport operator is fulfilling the hydrogen demand in a reliable and cost-efficient way. There are three main supply chain options, each associated with different expenses and transport capacities. Hydrogen can be transported either in a gaseous or liquid state or be produced directly at the airport so that only electricity needs to be transmitted from the renewable energy source. However, in all cases, the gaseous hydrogen (GH2 ) must be liquified at a certain point in the supply chain. The overall costs during transport and storage depend on the specific setting of the airport, such as the electricity connection, hydrogen sources, hydrogen demand, and the availability of transportation infrastructure (Hoelzen et al., 2022). A comprehensive evaluation of the various supply options, especially the quantification of economic performance indicators, is missing so far. This article aims to analyze, evaluate, and compare different hydrogen supply chain options for airports. The model-based assessment approach accounts for different combinations of individual components and the influence of surrounding parameters, while demand fulfillment is evaluated in terms of economic criteria. This will be addressed by our approach presented in the next sections. The remainder of this article is organized as follows: Sect. 2 explains the technological and logistical characteristics of the hydrogen supply chain. Section 3 introduces the modeling and assessment approach. Section 4 analyzes different hydrogen supply options for a generic reference airport, and Sect. 5 discusses the results’ insights and implications for airport operators.

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2 Characterization of the Hydrogen Supply Chain This section gives an overview of the technological, logistical, and economic characteristics of the hydrogen supply chain for aviation, comprising hydrogen production and liquefaction (2.1), as well as hydrogen transport and storage (2.2). 2.1 Hydrogen Production and Liquefaction Hydrogen can be produced in different ways. The production methods and feedstocks are usually distinguished by assigned color labels. An overview of the hydrogen colors and typical production costs is provided in Table 1. Green hydrogen is the most promising option in terms of environmental impacts. It is produced using water and electricity based on 100% renewable energy. During the electrolysis, water is split into oxygen and hydrogen. Depending on the combination of renewable energy sources in the electricity mix, the production costs for green hydrogen can differ significantly. Here, especially electricity generated by different solar technologies has a huge impact on the production cost of green hydrogen (Kayfeci et al., 2019). If the electricity mix used in the electrolysis consists of renewable and fossil sources, the hydrogen produced from the water is identified as yellow hydrogen. Red/pink/violet hydrogen indicates the use of nuclear energy during the electrolysis (Kayfeci et al., 2019; Newborough and Cooley, 2020). Using natural gas as a feedstock for hydrogen production gives different options to split carbon and hydrogen. Turquoise hydrogen, with the product of GH2 and solid carbon, is produced from natural gas using pyrolysis. If steam reforming is used for hydrogen production with natural gas as feedstock, carbon CO2 is the second educt of the process. The color of the hydrogen depends on the method of CO2 treatment. If CO2 is emitted into the atmosphere, hydrogen is marked as grey hydrogen, while blue hydrogen indicates further processing or capture and storage of CO2 . Increasing natural gas prices will directly influence grey and blue hydrogen prices. White hydrogen marks hydrogen, which is a waste product of other chemical processes, and if coal is used as feedstock, the hydrogen is called brown hydrogen. Regarding the emission reduction goals until 2050, green hydrogen is the most promising option for a more sustainable aviation sector. However, different challenges occur in terms of determining the production cost. First, the future hydrogen demand for aviation must be forecasted to ensure sufficient production quantities. It is important to consider the energy sector because a high amount of electricity generated by renewable sources is required for production and is one of the main cost drivers. In addition, different electrolyzer technologies are available on the market, which differ in their operating costs. Alkaline electrolysis is a well-established technology in the market with long-term stability and relatively low costs. In contrast, the proton exchange membrane electrolyzer (PEM) has high component costs, and it is not as commercialized but can run with high current density. The PEM system efficiency is higher due to a more rapid cell reaction and compact system structure than the alkaline electrolysis (Guo et al., 2019). The solid oxide electrolyzer is the least mature technology and is still in the research and development stage. This steam electrolysis allows an efficiency of over 90% with a relatively low electricity requirement of 2 kWh/Nm3 H2 (Komarov et al., 2021; LeValley et al., 2014). Here, the electrolysis technology with the lowest

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Table 1. Overview of hydrogen production pathways (Bhandari et al., 2014; Kayfeci et al., 2019; Sánchez-Bastardo et al., 2021) “Color”

Production method

Brown

Cost [EUR/kg]

Life Cycle Emissions in kg CO2eq /kg H2

Gasification of coal Coal

1.34

12

Grey

Steam reforming

Natural gas with CO2 released into the atmosphere

2.08

8

Yellow

Electrolysis

Water with a mixture of renewable and fossil energies

3.5–6.87

1–31

Blue

Steam reforming

Natural gas with CO2 2.27 captured and stored or processed industrially

4,8

Turquoise Pyrolysis

Natural gas with solid carbon as co-product

1.59–1.70

4,5

Red/ pink/ purple

Electrolysis

Water with nuclear power

4.15–7.00

2

Green

Electrolysis

Water with renewable energy

5.78–23.37 1–2.5

Hydrogen as a waste product of other chemical processes

0

White

Hydrogen feedstock

0

cost should be selected so that hydrogen can be competitive with kerosene. The last challenge results from the physical characteristics of hydrogen. Under normal conditions, hydrogen has a density of 0.0899 kg/Nm3 , and the density of LH2 is 70.79 kg/m3 . With the same size, 787 times more hydrogen can be transported (Töpler and Lehmann, 2017). Normally, the hydrogen is under a pressure of 25 bar after the electrolysis process. It must be compressed or liquefied for storage or transportation, which requires energy and increases energy costs (Milewski et al., 2014). To cool down the hydrogen to − 253 °C, it must first be compressed to 30 bar. Afterward, it can be cooled down using liquid nitrogen in a heat exchanger to reach a temperature of approximately −190 °C. To liquefy the hydrogen, further compression is needed (Klell et al., 2018). 2.2 Hydrogen Transport Different transportation options exist based on the state of matter (gaseous or liquid). Table 2 gives an overview of the currently most discussed options for transport (Klell et al., 2018; Niermann et al., 2021). If the hydrogen occurs in a gaseous state, the first important step is to compress it for transport via pipeline or truck. While truck transport is more flexible in terms of delivery

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31

Table 2. Overview of hydrogen production transport options Transportation mode

Typical capacity

Pressure

GH2

Pipeline

variable

20–100 bar

x

Truck

720 kg

200–350 bar

x

Truck

4,500 kg

1 bar

LH2

x

to different use places, transportation via pipeline offers the possibility of transporting larger quantities. Additionally, the existing network for natural gas can be used for hydrogen transport, which might make pipeline transport more cost-efficient. However, pipeline modifications are necessary to avoid hydrogen embrittlement (Cerniauskas et al., 2020). To transport hydrogen in a liquid state, the GH2 must be liquified by cooling it down to −253 °C, and this temperature should be maintained to prevent the hydrogen from vaporizing. The hydrogen must be transported in cryogenic tanks, which can be mounted on truck trailers or train wagons. An important aspect here is the boiling loss during transportation, which is approximately 1.65% per day (Hoelzen et al., 2022). Finally, hydrogen can be used in various sectors, such as mobility (e.g., aviation or automotive) or heavy industry (e.g., steel). The respective advantageous configuration of transport options depends strongly on the intended use, distance, and transport amount.

3 Modeling and Assessment Approach In this section, the modeling and assessment approach for the analysis of hydrogen supply options is presented. Next, the calculation models for the different supply chain stages are detailed in Sect. 3.2. 3.1 Calculation of the Total Expenditures in the Hydrogen Supply Chain The economic assessment is based on the total expenditures (TOTEX) accrued along the hydrogen supply chain. The TOTEX comprise capital expenditures (CAPEX) and operational expenditures (OPEX). Both CAPEX and OPEX are influenced by various parameters that reflect the techno-economic characteristics of each stage (Mischner J, Fasold H-G, Kadner K, 2011). The total expenditures are expressed per kg of LH2 at the airport for better comparability. The following notation is used for the formal assessment model: i = 1, . . . , I

Stage of the hydrogen supply chain (e.g., compression, liquefaction, transport)

k = 1, . . . , K

Production factors required in the supply chain (e.g., electricity, labor, consumables)

cLH2

Total expenditures accrued along the considered stages of the supply chain normalized to the provision of 1 kg LH2 [e/kg] (continued)

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(continued) ciTOTEX

Total annualized expenditures at stage i [e/year]

ciCAPEX ciOPEX d LH2

Capital expenditures at stage i [e]

xi

Hydrogen flow at stage i [kg/year]

aik

Consumption rate of production factor k at stage i [e.g., kWh/kg, h/kg, kg/kg, l/kg]

pk

Market price of production factor k [e/kWh, e/h, e/kg, e/l]

bi

Capacity (maximum hydrogen throughput) at stage i [kg/year]

δi

Hydrogen loss at stage i relative to throughput [%]

τi

Equipment service life at stage i [years]

si

Transportation distance at stage i [km]

ri

Transport duration at stage i [hours]

ni

Quantity of required equipment at stage i [pieces]

Operational expenditures at stage i [e/year] LH2 demand at the airport [kg/year]

For calculating the total supply chain expenditures per kg of LH2 at the airport, the annualized TOTEX of all supply chain stages are summed up and divided by the annual LH2 demand (Eq. (1)). I c

LH2

=

TOTEX i=1 ci d LH2

(1)

The annualized TOTEX of each stage are derived from the CAPEX and the OPEX (Eq. (2)). The CAPEX corresponds to the initial investment that has to be made for the required equipment and typically depends on the installed capacity. They are annualized using the expected service life of the respective equipment. The OPEX capture the annual expenditures for maintenance and production factors, such as electricity, labor, and consumables. ciTOTEX =

ciCAPEX + ciOPEX τi

(2)

The CAPEX and OPEX are influenced by various parameters, detailed for the individual supply chain stages in the following.

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Liquefaction The CAPEX of a liquefaction plant is mainly driven by its capacity. According to (Reuß et al., 2019), they can be modeled using an exponential function that considers the ratio of the selected capacity of the liquefaction plant in relation to the capacity of a baseline plant, the CAPEX of that baseline plant, and the size degression coefficient ∈ (Eq. (3)). The main OPEX components of a liquefaction plant are maintenance, electricity consumption, and liquid nitrogen consumption. While the annual maintenance expenditures can be estimated as a percentage of the CAPEX, the expenditures for electricity and liquid nitrogen consumption are proportional to the realized hydrogen flow as well as the market prices of these production factors (Eq. (4)).   bLiquefaction ∈Liquefaction CAPEX CAPEX = cBaseline · (3) cLiquefaction bBaseline     Electricity OPEX CAPEX cLiquefaction + aLiquefaction · xLiquefaction · pElectricity = cMaintenance cLiquefaction   Nitrogen + aLiquefaction · xLiquefaction · pNitrogen (4) Compression The estimation of CAPEX and OPEX for the compression stage is similar to the liquefaction stage. While the CAPEX mainly depends on the capacity of the compressor, the OPEX are primarily driven by maintenance and electricity consumption (Eqs. (5) and (6)).   bCompression ∈Compression CAPEX CAPEX cCompression = cBaseline · (5) bBaseline     Electricity OPEX CAPEX cCompression + aCompression · xCompression · pElectricity (6) = cMaintenance cCompression Truck Transportation The CAPEX of truck transportation is influenced by the number of tractor and trailer units required to fulfill the transportation demand, which can be derived from the desired transportation capacity and the duration of each trip. The OPEX at that stage is composed of maintenance expenditures (again, estimated as a function of CAPEX), fuel consumption expenditures (a function of distance and fuel price), and personnel expenditures (a function of trip duration and driver’s wage) (Eqs. (7) and (8)).

OPEXTruck

CAPEXTractor = n · pTractor CAPEXTrailer = n · pTrailer     Maintenance CAPEX Maintenance CAPEX cTraktor + cTrailer cTrailer = cTractor   Fuel Driver + aTruck (si ) · pFuel + aTruck (ri ) · pDriver · n

(7)

(8)

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Pipeline transportation Finally, the CAPEX related to pipeline transport are mainly driven by the length and the diameter of the pipeline, which are influenced by transportation distance and capacity, respectively (Reuß et al., 2019). While a linear relationship between length and CAPEX can be assumed, the relationship between diameter g and CAPEX follows an exponential function. The OPEX of a pipeline are primarily driven by maintenance (again, estimated as a fraction of the CAPEX) as well as electricity consumption (a function of throughput and distance) (Eqs. (9) and (10)). CAPEX CAPEX = cBaseline · e(ρ·g (bPipeline )) · spipeline cPipeline

   Electricity  OPEX Maintenance CAPEX cPipeline cPipeline + aPipeline sPipeline · xPipeline · pElectricity = cPipeline

(9) (10)

4 Case Study: Comparison Supply Options for Green Hydrogen In this section, a case study on the comparison of hydrogen supply options for green hydrogen is conducted. For this purpose, the assumptions and deployment options are introduced in Sect. 4.1, and the relevant expenditures and parametrizations for each component are further explained in Sect. 4.2. Section 4.3 determines the expenditures per kilogram of transported hydrogen for the different supply options. 4.1 Supply Chain Configurations The supply chain configurations differ in the transportation options and the position of the liquefaction inside the hydrogen supply chain (see Fig. 1). For the illustration of the supply chain, the following assumptions are made. Since only green hydrogen is considered, the expenses of hydrogen production are not relevant in this case, and the electrolysis costs are not further described. For the case study, the distance between electrolysis and the airport can be compared to the distance between the Hamburg airport and an onshore connection point to the offshore wind parks in the North Sea. The value of the different costs is based on the following conversion: For USD, it is assumed that 1 USD equals the amount of 1 EUR (Deutsche Bundesbank, 2022a). For NOK, it is assumed that 10 NOK equals the amount of 1 EUR (Deutsche Bundesbank, 2022b). For the evaluation, it is assumed that the electrolyzer is built near renewable energy plants to limit the losses during electricity transmission. Every transportation mode is available to transport the required amount of hydrogen, and during transportation via truck, no congestion or other delays are considered. Along the supply chain, storage is not considered, so it is assumed that the hydrogen is transported just in time and is directly used in the next stage. The losses are considered after and while transporting the hydrogen. Construction times are not considered. The static demand of one year is assumed. For the description of the green hydrogen supply chain network, different deployment options are considered. The deployment options deal with the uncertainty of using the natural gas pipeline network (entire, 50%, or nothing). At the same time, the other transportation modes are considered under the same conditions in all deployment options.

Evaluation of Hydrogen Supply Options for Sustainable Aviation

35

Fig. 1. Supply chain configurations

4.2 Parametrization. All parameters for the case study in Sect. 4.3 be found in Appendix A. The parameterization of the liquefaction is based on (Reuß et al., 2017), including the required amount of electricity and the expenditures for operation and management. The nitrogen price is based on (Atlas Copco, 2021) and the amount on (Bracha et al., 1994). For the fuel price, the parametrization is based on (ADAC, 2022), the fuel consumption can be found in (Teichmann et al., 2012), and the electricity price can be found in (BDEW, 2022; Eurostat, 2021). The assumptions for the compressor design and electricity consumption can be found in (Mayer et al., 2019; Reuß et al., 2017; Ulleberg and Hancke, 2020). The pipeline layout is based on (Cerniauskas et al., 2020), including the investment, operation, management, and electricity needed to maintain constant pressure. The investment amount for trucks and trailers is based on (Niermann et al., 2021). The scale of capacity, annual maintenance, and average speed can also be found in (Niermann et al., 2021). The considered demand for the case study is 80,600 t per year and can be found in the base-case scenario in (Hoelzen et al., 2022). 4.3 Results The expenditures per kg H2 of the described deployment options are shown in Fig. 2. Since the same amount of hydrogen is considered in all deployment options, the expenses for liquefaction (2.15 e/kg H2 ) are almost the same. The marginal exception here is the option of transporting LH2 . Due to boil-off during transportation, more hydrogen must be liquefied in advance. The expense for liquefaction when transporting liquid hydrogen is 2.16 e/kg H2 . The most significant difference in expenses can be seen in the deployment option of gaseous truck transportation. Here, expenses of 1.91 e/kg H2 occur for hydrogen compression and 0.58 e/kg H2 for transport, resulting in the highest total expense of 4.65 e/kg H2 . The expense of hydrogen transport via pipeline depends on the length of existing natural gas pipelines (entire, 50%, or nothing) and could be

36

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0.22 e/kg H2 , 0.24 e/kg H2 , and 0.26 e/kg H2 . The different scenarios result in overall expenses for the deployment option of pipeline transportation of 2.38 e/kg H2 , 2.39 e/kg H2 , and 2.41 e/kg H2 , and for LH2 truck transportation, 2.26 e/kg H2 . Transportation expenses for LH2 truck transportation are in the amount of 0.10 e/kg H2 .

Fig. 2. Case study result for the different deployment options

The results show that transporting GH2 via truck is economically unattractive due to high expenses for transportation (0.58 e/kg H2 ) and high expenses for the compressor (1.91 e/kg H2 ). In this regard, truck transportation of LH2 is the most suitable option. Especially the compression of hydrogen is a key driver of the expenses next to the transportation, which is avoided in the case of LH2 transport via truck. The expenses for hydrogen transport via pipeline differ slightly from the LH2 transport via truck. In this case, distance and demand are implicated in transportation expenses. How exactly distance and demand are involved in transportation expenses and how this affects the choice of transportation mode was also studied. For this purpose, Fig. 3 shows different scenarios for transport distance and demand to determine the break-even point between pipeline transport and truck transport for LH2 . The breakeven point is between the demand of 150 and 200 t/day and a distance between 50 and 100 km. For a demand higher than 150 t/day, pipeline transport is the most economical, and for a demand lower than 150 t/day, truck transport is the most economical. For a transported demand of 150 t/day, truck transport is only beneficial with a transport distance of 50 km; otherwise, pipeline transport is beneficial. Overall, pipeline transport is more advantageous when the demand is high than truck transport when the demand is low. The transport distance has only a minor influence. Considering the current insecure market situation concerning energy prices (doubling), the following stages of the hydrogen supply chain are influenced: Doubling fuel prices would increase transportation expenses when using truck transportation. The

Evaluation of Hydrogen Supply Options for Sustainable Aviation

37

expenses will increase by 0.3 e/kg H2 (truck liquid) and 0.2 e/kg H2 (truck gaseous). If the electricity prices double, the total hydrogen price will be 2 e/kg H2 higher for every supply option. An example calculation for configuration one is given in Appendix B.

Fig. 3. Comparison of the hydrogen price for different demands and distances

5 Discussion and Conclusion The article compares different supply options for hydrogen and highlights the expenditures associated with the transportation modes. Here, the analysis shows that there is not only one economically optimal supply option for hydrogen, but the economically advantageous supply option depends on various parameters. For an LH2 demand, transport in the same aggregate state is the most expense-effective solution up to the demand of 150 t/day. For higher demands, gaseous pipeline transportation is the most economical option. In particular, the quantity to be transported is of great relevance. In addition, the study reveals that the boil-of-losses while transporting LH2 have relevance because of the large energy consumption during liquefaction and the resulting high expenses. Reducing energy consumption during hydrogen liquefaction or reducing the boil-off loss during LH2 transportation would positively influence the economic efficiency of the considered transportation mode. However, the study carried out is subject to some limitations. To simplify the investigated supply chains and to concentrate on the essentials, assumptions are made to reduce complexity. The economic analysis includes three transportation modes, while the import or export of hydrogen is not considered. This restriction is made to understand the basics behind the expenditures for hydrogen supply options. In further research, more transportation modes, as well as the import and export of hydrogen, must be considered. This can be adapted with an extension of the supply chain. Especially in the case of hydrogen import, the expenses for transport might be lower, resulting in lower overall expenses for the supply chain. For transportation modes running on fuels, other powertrains powered with renewable energy must be considered in future research.

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Hydrogen can be used in many industries, such as automotive and steel, and it is questionable whether enough hydrogen is available to cover the entire demand. Apart from these facts, the overall demand for hydrogen is highly uncertain. Using hydrogen in different industries requires a more holistic supply chain model and analysis, considering the need, dependencies, and links between the industries. In addition to expanding the hydrogen supply chain across more sectors and components, it is also important to analyze the supply chain over time by considering multiple periods. These research gaps must be addressed in future work. Due to the expenditure-oriented evaluation in this article, depreciation and calculative interest are not considered, and environmental aspects are out of scope. Considering environmental aspects next to economic aspects can help to configure more sustainable supply chains. However, hydrogen is an alternative for sustainable aviation, but only for short-up to middle-range flights, which makes hydrogen just a part of sustainable aviation. Still, other fuels and technologies are needed. In future research, the findings of this article can be used to design a hydrogen supply network to fulfill a hydrogen demand at one or several specific airports. The supply chains are based on the assumption that the demand at the airports can only be met by liquid hydrogen. The supply chain may differ for gaseous hydrogen demand in other sectors. Liquefaction is not necessarily required if hydrogen can be transported in a gaseous state. Depending on the demand, a decision can be made with this basic formulation when setting up the hydrogen network regarding which means of transport will incur the least expenses. Even in other sectors, this basic model formulation can be used to give a first overview of the expenses linked to every supply option. Adding different modes for LH2 or GH2 transportation is a useful extension of the problem definition. With the model, a basis for developing hydrogen supply networks is given, which can now be used in the next step to make further decisions toward hydrogen-powered aviation.

Appendix Appendix A: Parametrization of the three supply chain configurations Stage i

Parameter

Configuration 1

Prices

pElectricity

0.2664 e/kWh

pNitrogen

0.2 – 0.35 e/l

pFuel

2 e/l

pDriver

35 e/h

CAPEX cLiquefaction

105 million e

bLiquefaction

83,000 t/year

τi

20 years

Liquefaction

  CAPEX cMaintenance cLiquefaction

Losses Nitrogen

aLiquefaction



Configuration 2

bLiquefaction 50 t/day

Configuration 3

0.66

82,000 t/year

82,000 t/year

4% 1.65% 100,000 l/year (continued)

Evaluation of Hydrogen Supply Options for Sustainable Aviation

39

(continued) Stage i

Parameter Electricity

Configuration 1

aLiquefaction

6.78 kWh/kg

CAPEX cCompression

15,000 e

bCompression

-

τi

15 years



Compression

  CAPEX cMaintenance cCompression

Losses Electricity

Transport

Configuration 2

bcompression 1 kW

Configuration 3

0.6089

-

81,000 t/year

4% 1.96 kWh/kg

aCompression

0.5%

CAPEX cTransport

pTractor : 160,000 e

292.152∗

pTrailer : 860,000 e

0.0016·250 mm mm e

sTransport

-

100% new: 190 km 50% new: 95 km 0% new: 0 km

-

τi

Tractor : 8 years Trailer : 12 years

40 years

Tractor : 8 years Trailer : 12 years

Tractor : 12% Trailer : 2%

5 e/m

Tractor : 12% Trailer : 2%

Fuel: 30 l/100 km

Electricity: 0.82 kWh/kg

Fuel: 30 l/100 km

  Maintenance cCAPEX cTransport Transport Energy

aTransport



pTractor : 160,000 e



· spipeline

pTrailer : 550,000 e

Losses

1.65%

0.5%

0%

n

25

1

155

Driver (r ) aTruck i

8 h/day

-

8 h/day

Appendix B: Calculation of the case study Configuration 1: xLiquefaction = 80,600,000 kg · 1.65% · 1.65% = 83,281,743.4 kg

(3)

(4)

(2) xLiquefaction = 80,600,000 kg · 1.65% = 82,125,000 kg

40

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n=

82,125,000 kg  = 25  days 4,500 kg · 2 tours · 365 day year

(7)

(8)

(2) (1)

References ADAC: Durchschnittlicher Preis für Diesel-Kraftstoff in Deutschland vom 7. Januar 2014 bis zum 27. September 2022 [Online], Statista. https://bit.ly/3EiRqWe. Accessed 8 Oct 2022 Atlas Copco (ed) (2021) Was kostet Stickstoff? [Online]. https://bit.ly/3AKymiX. Accessed 15 Sept 2022 Barke, A., Thies, C., Melo, S.P., Cerdas, F., Herrmann, C., Spengler, T.S.: Comparison of conventional and electric passenger aircraft for short-haul flights – a life cycle sustainability assessment. Procedia CIRP 105, 464–469 (2022) BDEW (2022) Industriestrompreise* (inklusive Stromsteuer) in Deutschland in den Jahren 1998 bis 2022 [Online], Statista Bhandari, R., Trudewind, C.A., Zapp, P.: Life cycle assessment of hydrogen production via electrolysis – a review. J. Clean. Prod. 85, 151–163 (2014) Boeing (ed): Commercial market outlook: 2022–2042 (2022). https://www.boeing.com/commer cial/market/commercial-market-outlook/index.page. Accessed 15 Aug 2022 Bracha, M., Lorenz, G., Patzelt, A., Wanner, M.: Large-scale hydrogen liquefaction in Germany. Int. J. Hydrogen Energy 19(1), 53–59 (1994) Burgueño Salas, E.: Global air traffic - scheduled passengers 2004–2022 (2022). https://bit.ly/2rN TzVm. Accessed 29 Aug 2022 Cerniauskas, S., Jose Chavez Junco, A., Grube, T., Robinius, M., Stolten, D.: Options of natural gas pipeline reassignment for hydrogen: cost assessment for a Germany case study. Int. J. Hydrogen Energy 45(21), 12095–12107 (2020) Crittenden, M.: Ultralight batteries for electric airplanes. IEEE Spectr. 57(9), 44–49 (2020) Deutsche Bundesbank (ed): Euro-Referenzkurs der EZB / 1 EUR = … USD / Vereinigte Staaten (2022a). https://bit.ly/3Go7hpl. Accessed 17 Nov 2022a

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Deutsche Bundesbank (ed): Euro-Referenzkurse der Europäischen Zentralbank: Jahresendstände und –durchschnitte (2022b). https://bit.ly/3AmOg2w. Accessed 17 Nov 2022b European Commission, Directorate-General for Mobility and Transport and Directorate-General for Research and Innovation: Flightpath 2050: Europe’s vision for aviation: maintaining global leadership and serving society’s needs, Publications Office (2011) Eurostat: trompreise nach Art des Benutzers (2021). https://bit.ly/3Ol66cc. Accessed 22 Jul 2022 Farokhi, S.: Future Propulsion Systems and Energy Sources in Sustainable Aviation. Wiley (2019) Graver, B., Rutherford, D., Zheng, S.: CO2 Emissions from commercial aviation: 2013, 2018 and 2019 [Online], International Council on Clean Transportation, Washington DC (2020) Guo, Y., Li, G., Zhou, J., Liu, Y.: Comparison between hydrogen production by alkaline water electrolysis and hydrogen production by PEM electrolysis. IOP Conf. Ser. Earth Environ. Sci. 371(4), 42022 (2019) Hoelzen, J., Flohr, M., Silberhorn, D., Mangold, J., Bensmann, A., Hanke-Rauschenbach, R.: H2-powered aviation at airports – design and economics of LH2 refueling systems. Energy Convers. Manage. X 14, 100206 (2022) Jungbluth, N., Meili, C.: Recommendations for calculation of the global warming potential of aviation including the radiative forcing index. Int. J. Life Cycle Assess. 24(3), 404–411 (2018). https://doi.org/10.1007/s11367-018-1556-3 Kayfeci, M., Keçeba¸s, A., Bayat, M.: Hydrogen production. In: Solar Hydrogen Production, pp. 45–83. Elsevier (2019) Klell, M., Eichlseder, H., nd Trattner, A.: Wasserstoff in der Fahrzeugtechnik, Wiesbaden. Springer Fachmedien Wiesbaden (2018). https://doi.org/10.1007/978-3-658-20447-1 Komarov, I.I., Rogalev, A.N., Kharlamova, D.M., Yu Naumov, V., Shabalova, S.I.: Comparative analysis of the efficiency of using hydrogen and steam methane reforming storage at combined cycle gas turbine for cogeneration. J. Phys. Conf. Ser. 2053(1), 12007 (2021) Kords, M.: Anteil der Verkehrsträger an den weltweiten CO2 -Emissionen aus der Verbrennung fossiler Brennstoffe in den Jahren 2018 und 2019, IEA (2022). https://bit.ly/2ItYc0z. Accessed 15 Aug 2022 Lee, D.S., et al.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmos. Environ. (Oxford, England: 1994) 244, 117834 (2021) LeValley, T.L., Richard, A.R., Fan, M.: The progress in water gas shift and steam reforming hydrogen production technologies – a review. Int. J. Hydrogen Energy 39(30), 16983–17000 (2014) Mayer, T., Semmel, M., Guerrero Morales, M.A., Schmidt, K.M., Bauer, A., Wind, J.: Technoeconomic evaluation of hydrogen refueling stations with liquid or gaseous stored hydrogen. Int. J. Hydrogen Energy 44(47), 25809–25833 (2019) Milewski, J., Guandalini, G., Campanari, S.: Modeling an alkaline electrolysis cell through reduced-order and loss-estimate approaches. J. Power Sources 269, 203–211 (2014) Mischner, J., Fasold, H.-G., Kadner, K.: Gas2energy.net: Systemplanerische Grundlagen der Gasversorgung, Deutscher Industrieverlag (2011) Newborough, M., Cooley, G.: Developments in the global hydrogen market: the spectrum of hydrogen colours. Fuel Cells Bull. 2020(11), 16–22 (2020) Niermann, M., Timmerberg, S., Drünert, S., Kaltschmitt, M.: Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen. Renew. Sustain. Energy Rev. 135, 110171 (2021) Ponater, M., Marquardt, L., Ström, K., Gierens, R., Sausen, R.: On the potential of the cryoplane technology to reduce aircraft climate impact [Online], Friedrichshafen, DLR-Institut für Physik der Atmosphäre (2003). https://bit.ly/3X7kBnP. Accessed 11 Nov 2022 Pornet, C., Isikveren, A.T.: Conceptual design of hybrid-electric transport aircraft. Prog. Aerosp. Sci. 79, 114–135 (2015)

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Pricing and Greening Level Decisions in a Two-Stage Hydrogen Supply Chain Considering State Subsidies and Taxes Maria Beranek(B) , Anna Sch¨ utze, and Udo Buscher Faculty of Business and Economics, Technische Universit¨ at Dresden, 01062 Dresden, Germany {maria.beranek,anna.schuetze,udo.buscher}@tu-dresden.de

Abstract. Green hydrogen as an energy carrier is a beacon of hope to achieve climate goals because of its potential to reduce carbon emissions. Currently, the hydrogen demand cannot be met entirely with green hydrogen. Thus, non-green hydrogen cannot yet be waived. Consequently, this paper considers a two-stage hydrogen supply chain (SC) consisting of three manufacturers producing green, partially green, and non-green hydrogen and a retailer. In addition to retail and wholesale prices, the manufacturer of partially green hydrogen must determine how green its produced hydrogen should be. The players are under the influence of governmental instruments, i.e., taxes on producing non-fully green hydrogen and subsidies for the retailer for selling green hydrogen. In a manufacturer-led Stackelberg game, we observe that it is more purposeful to promote green technologies through incentives than to force them by taxes. For the partially green manufacturer, relying exclusively on green hydrogen is advantageous if its market share is high, if the additional costs for the conversion to green hydrogen are comparatively low, or if the green sensitivity of the customers is high.

1

Introduction

As early as 1875, the novelist Jules Verne notes, “Water will be the coal of the future. Tomorrow’s energy is water that has been decomposed by electric current.” [1] However, the outstanding importance of hydrogen, which arises from the described splitting of water, has been rediscovered recently. In addition to its applications in, e.g., transportation, heating, and industrial processes, hydrogen has the advantage that its usage does not cause carbon emissions. It can be considered a green energy carrier if produced using renewable energy sources. In order to achieve the agreed climate targets by 2050 based on the Paris Agreement of 2016 [2], and thus, a carbon dioxide-neutral future, the German government ascribes central importance to hydrogen produced from renewable energy sources. This goal will be achieved if sufficient renewable energy is available for hydrogen production. However, other technologies are also competing for scarce renewable energy sources. Therefore it is expected, at least for a transitional c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 43–63, 2023. https://doi.org/10.1007/978-3-031-38145-4_3

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period, that hydrogen demand will have to be met partially by hydrogen produced by non-green energy sources. In order to stimulate the diffusion of green hydrogen, the German funding program H2-Global proposes a double auction model that bridges the gap for a retailer between the high prices of hydrogen and the low prices at which hydrogen is competitive [3]. We take up these ideas and present a simple game theoretic decentralized hydrogen SC model in our paper, consisting of a green, a non-green, and a mixed (partially green) manufacturer. Each manufacturer produces and sells substitutable hydrogen. That allows an authentic representation of the hydrogen market where hydrogen is produced in different color ranges of scale [4], representing the hydrogen’s environmental impact. Current research, on the other hand, mainly focuses on two kinds of manufacturers [5–8] or just two types of products [4,9–11]. Our study investigates the conditions under which the mixed manufacturer is more likely to be positioned in the green or non-green manufacturer area when there are government interventions, cross-price elasticities, and different levels of market share. To support the distribution of green hydrogen, we consider a subsidized retailer and taxes imposed on the production of greenhouse gases. That leads us to the following research questions: 1. What greening level does the mixed manufacturer choose in competition with two other fixed green and non-green manufacturers? Furthermore, to what extent do parameters like market share, customer green awareness, costs and taxes influence the greening level of the mixed manufacturer? 2. How far does the state steer the greening strategy through taxes and subsidies for the retail sector? In this paper, firstly, we provide in Sect. 2 a literature review organized by the general research areas. Section 3 introduces the mathematical model and sets up the manufacturer-led Stackelberg game with the demand and profit functions of all SC members. Section 4 presents the solution approach, including different cases which are formally distinguished and a final listing of the solution procedure. Section 5 introduces a numerical example, followed by a sensitivity analysis before concluding in Sect. 6.

2

Literature Review

Within the Hydrogen Supply Chain and the Green Supply Chain, several investigations exist about the members’ price, quantity, and greening strategies by using game-theoretic models. We have developed a mathematical model including three manufacturers. The mixed manufacturer sets its greening level as a decision variable, and the retailer receives subsidies for buying green hydrogen while the manufacturers pay taxes for carbon emissions. In the following, we introduce the research fields of Hydrogen Supply Chain and Green Supply Chain, which provide the foundations for our model. These two fields are viewed in the context of governmental interventions and game-theoretic modeling. Studies about the Hydrogen Supply Chain investigate, e.g., the hydrogen production capacity differences to optimize the SC. Some game-theoretic models

Pricing and Greening Level Decisions

45

analyze green and blue hydrogen pathways [4,9–11]. In case studies on South Korea and the UK, they determine optimal import quantities of green and blue hydrogen [12] and optimize production costs [13]. Lower-cost hydrogen serves the current market potential but causes more carbon emissions. That leads to the need for governmental interventions to create incentives for a more environmentally economy through taxes, subsidies, and cap-and-trade regulation [14– 16]. Yue and You [17] investigate a bilevel mixed integer nonlinear programming model to design the optimal structure of a three-echelon SC within a Stackelberg game. The middle member of the SC, the biorefinery, makes the most significant profit inside the three-echelon decentralized SC. Flores-Perez et al. [18] study the hydrogen SC in a mixed-integer bi-level programming (MIBLP) approach as a Stackelberg game. They prove the methodological approach successful in optimizing one-leader multi-objective or multi-follower single-objective approaches. Jafari, Safarzadeh and Azad-Farsani’s [19] game-theoretic study underlies a Nash and Stackelberg model. They analyze the strategy of a manufacturer of hydrogen that wants to increase the energy efficiency of its cars under government incentives. Demand and profits increase if the efficiency rate increases, e.g., through subsidies. Cao et al. [20] present a business model for storing byproducts from chemical plants, such as hydrogen, and are looking for an equilibrium between transport and trading strategies. They find that a coalition between chemical plants will increase their profits and more efficiently use resources. Further work in Green Supply Chain management research offers various models to analyze the competition between green and non-green manufacturers. That models are abstract and can, e.g., apply for hydrogen units [21–23]. Madani and Rasti-Barzoki [8] set the government as the leader, affecting the competition between green and non-green SCs through tariffs. The increase in subsidy rates had a more significant impact on government and SC profits and the greening level than an increase in tax rates. Basiri and Heydari [24] suggest a model for a two-echelon SC, which offers a non-green product. The manufacturer plans to introduce a substitutable green product by deciding about the product’s green quality, and the retailer decides on the selling price and the level of sales effort. The centralized model generates the highest profits in the three scenarios examined: decentralized, centralized, and cooperative. Zhang et al.[7] study the competition between two manufacturers under cap-and-trade regulation. They find that the larger the manufacturer, the higher the probability he produces a green product. Sana [25] considers two manufacturers’ substitutable products, i.e., green and non-green. They also illustrate that a centralized model is more profitable than a decentralized one and that the green sensitivity of customers significantly impacts the implementation of green products in the market. Liu, Ke and Tian [14] investigates an SC with a retailer and two manufacturers that produce substitutable products under cap and trade. The customers’ green sensitivity is also a critical factor for green production. The retailer benefits from the manufacturer’s investment in green production, so he is incentivized to motivate the manufacturer to green production. Mondal and Giri [26] study a two-echelon green SC with two manufacturers and one retailer under govern-

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ment intervention. The government offers subsidies to the green manufacturer for supplying each unit of a green product with a minimum level of greening. The manufacturer receives a subsidy or penalty if it does not meet the minimum level. The centralized model is only beneficial for all SC members if the non-green manufacturer has previously established profit-sharing contracts. The government should set a suitable green minimum and further adjustment factors. Barman et al. [27] consider a two-echelon SC, in which one manufacturer sells a green and the other a non-green product. With government intervention, the selling prices of green products increase, thus their profitability, while prices of non-green products decrease, compared to a model without government intervention. Ma et al. [5] consider an SC in which a manufacturer invests in green emission reduction technologies (GERT) and the retailer in technology to disclose the greening level of products to consumers more transparently. They find that government subsidies lead to emission reductions and improve profits for all SC members, but high emission reduction standards can mitigate these effects. Again, a centralized model will lead to a more efficient SC and lower carbon emissions. Table 1 lists the literature that inspired the design of our model and are methodological closest to our paper. Table 1. Comparative literature survey on modeling two-echelon SC under government intervention Author(s)

Number of parties

Subsidies

Taxes

Greening Level

Stackelberg model

Madani and Rasti-Barzoki (2017) [8]

2 M, 2 R

C

C





Basiri and Heydari (2017) [24]

1 M, 1 R

Zhang et al. (2018) [7] Liu, Ke and Tian (2020) [14] Mondal and Giri (2021) [26] Barman et al. (2021) [27]

2 M, 1 R 2 M, 1 R

C

Ma et al. (2021) [5]

1 M, 1 R

M

Sana (2020) [25]

2 M, 1 R

This paper

3 M, 1 R

 

2M

C













 R

M





(M: Manufacturer, R: Retailer, C: Customer)

Madani and Rasti-Barzoki [8], Mondal and Giri [26], Ma et al. [5], and Barman et al. [27] propose that the mathematical models could extend to more than one SC, manufacturer, retailer, or product to obtain a more realistic simulation of the real-world market. As best as the author’s knowledge, we are linking up to it for the first time and modeling a hydrogen SC with three manufacturers. In order to make the purchase of the green product economically attractive, the retailer receives subsidies for selling green hydrogen, and the manufacturers pay taxes for carbon emissions.

Pricing and Greening Level Decisions

3 3.1

47

The Hydrogen Supply Chain Model Model Description

In the following, we address fundamental assumptions regarding the two-echelon hydrogen SC. All notations used in the paper are summarized in Table 2. Please note that in this table, all input parameters are normalized to values between 0 and 1. That simplifies the mathematical analysis without sacrificing the fundamental model relationships and is a common assumption in literature [28–30]. • Recall that we consider a hydrogen SC consisting of three manufacturers, who have the market power and act as Stackelberg leaders, and a retailer, the Stackelberg follower. Based on Mierscher et al. [31], our model assumes that the hydrogen market is a supply oligopoly. Only a few manufacturers hold vast amounts of hydrogen. Furthermore, we see the manufacturers in the position to be the first in the supply chain to set their prices, as these are highly dependent on current raw material costs. Therefore, manufacturers are characterized by having the market power and used to be Stackelberg leaders. With this model, we represent the standard framework in a Stackelberg SC [32]. The manufacturers compete in selling the substitutable hydrogen, which can be positioned in different color ranges of a scale, representing the environmental impact caused by the production process. The green manufacturer produces green hydrogen (g) based on renewable energy sources. It causes the lowest carbon emissions of all types of hydrogen. As in [14], we simplistically assume the carbon emissions of green hydrogen to be zero. Carbon emissions that occur on transport routes, e.g., are neglected. In our model, we further assume that a non-green manufacturer produces grey hydrogen (n) based on steam reforming of fossil fuels such as natural gas, coal, or oil, causing the highest carbon emissions of all hydrogen colors. Besides, we consider a mixed manufacturer who has yet to position itself to the market by choosing the type of hydrogen it wants to produce, ranging between green and grey on the color scale (m). • The monopolistic retailer buys all types of hydrogen from the manufacturers and distributes them to customers. The retailer could, e.g., be the operator of a hydrogen filling station. We assume that customers pay different prices for different types of products but cannot influence the composition of the product mix they ultimately receive, as, e.g., in the German electricity market. Similarly, the hydrogen retailer purchases the quantities of hydrogen demanded but does not separate them in such a way that, e.g., green hydrogen is sold only to green customers. Due to the same functionality, customers instead buy a hydrogen mix, but of course, which influences the quantities produced by the prices paid. • To reflect the position of hydrogen on the color scale and the associated carbon emissions, we adopt the concept of a greening level [24–27]. In contrast to the mentioned research, we set the greening level between 0 ≤ θi ≤ 1. Green

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hydrogen is assigned the highest greening level, i.e., θg = 1, and non-green hydrogen has the lowest greening level, i.e., θn = 0. The greening level of the mixed manufacturer is to be determined and can be positioned anywhere on the scale, i.e., 0 ≤ θm ≤ 1. As a consequence, we are able to use the greening level as a decision variable and as a parameter simultaneously and, therefore, distinguish our model from other literature. • The market demands for the three product types are deterministic and linearly dependent on the greening levels and retail prices, i.e., Di = ai − bpi + e (pj + pk ) + kθi

∀i, j, k ∈ {g, n, m}, i = j = k.

(1)

Table 2. Notations

Similar demand functions are commonly used in literature, see, e.g., [14,27]. Thereby, ai represents the market potential  of each hydrogen type i. Assuming an overall market potential of one, i∈{g,n,m} ai = 1 should hold. The

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parameter b is the customer’s sensitivity to the retail price pi . The cross-price elasticity parameter e represents the impact of the retail price of the competing products on demand for the own product. Furthermore, to represent the green awareness of the customers in the demand function, the greening level θi gets multiplied with the customers’ green sensitivity k, as, e.g., in [7,14,24]. For the special cases of θg = 1 and θn = 0, i.e., the green and the non-green hydrogen’s greening levels being fixed parameters, we obtain the following demand functions for each of the hydrogen types: Dg = ag − bpg + e (pm + pn ) + k Dn = an − bpn + e (pg + pm )

(2) (3)

Dm = am − bpm + e (pg + pn ) + kθm .

(4)

• For simplicity, we assume the unit production cost c0 to be the same for the three manufacturers. These production costs are independent of the respective production process of the hydrogen, i.e., also independent of the greening level, and include, e.g., administrative expenses. Additionally, we consider greening level dependent unit production costs c1 θi for each hydrogen type i [8,26,27]. See also Beranek and Buscher [33], who consider quality-dependent and quality-independent unit production costs in a similar way. That implies having higher costs for green hydrogen than for grey hydrogen. We base this assumption on the Hydex, a cost-based spot price index for hydrogen. This index showed significantly higher prices for green hydrogen in the past years, followed by blue and grey hydrogen [34,35], which is caused by high purchase prices of green electricity. If this changes in the future, the proposed cost function should be modified. Furthermore, to reflect fixed costs resulting from changes in the greening level, we consider greening level dependent fixed costs 12 ηθi2 . In literature, these costs are usually assumed to increase quadratically [8,14,26,27], which means that the more the greening level is to be improved, the more investments are necessary. E.g., a higher greening level may entail greater investments in research and development or production facilities [16]. On the other hand, if governmental measures supported those processes, η would decrease, and the greening level enhancement costs would also decrease. • To promote the diffusion of green hydrogen, we examine two government instruments in this paper - taxes and subsidies. First, we assume that, as is common in many countries, taxes must be paid for the carbon emissions caused. The specific form of these taxes may differ from country to country. In Germany, e.g., companies have to purchase emission rights in the form of certificates. For our paper, we assume that the manufacturers that cause carbon emissions must pay for them. The non-green manufacturer with θn = 0 causes the highest carbon emissions and has to pay a tax rate of Gt per unit, which should be interpreted as a common tax rate for the carbon generated during the production of one unit of grey hydrogen. The higher the greening level, the less carbon emissions occur, i.e., the less taxes must be paid. Generally,

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we assume a tax rate of Gt (1 − θi ) per unit. Hence, the green manufacturer does not have to pay any taxes. In contrast, we consider subsidies Gs as an instrument to promote the distribution of green hydrogen on the retail side. In Germany, e.g., retailers are supposed to receive subsidies as the production of green hydrogen is still relatively expensive in the first phase, and its distribution could otherwise lead to financial losses [3]. Please note that the subsidies only apply to the sale of green hydrogen. The mixed manufacturer can only obtain them if it opts for the maximum greening level. • Moreover, all parameters are known to all players. We are dealing with a symmetric game and, therefore, with symmetric information [17]. The illustration of the structure of the hydrogen SC and its material and cash flow shows in Fig. 1.

Fig. 1. Flow of goods and cash flow

3.2

Profit Functions

Taking all the considerations described above into account, the following profit functions result for the players. Manufacturers: 1 πM i = [wi − c0 − c1 θi − Gt (1 − θi )] · Di − ηθi2 2

∀i ∈ {g, n, m}

(5)

Taking the special cases of θg = 1 and θn = 0 into account, we obtain for the green manufacturer: 1 πM g = (wg − c0 − c1 ) · Dg − η 2

(6)

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51

non-green manufacturer: πM n = (wn − c0 − Gt ) · Dn

(7)

1 2 πM m = [wm − c0 − c1 θm − Gt (1 − θm )] · Dm − ηθm . 2

(8)

mixed manufacturer:

Retailer: As mentioned, the retailer only receives subsidies if θm = 1 and positions itself like the green manufacturer. To account for this circumstance in the mathematical analysis, we propose a case distinction concerning the retailer’s profit function: ⎧ ⎪ ⎪(pg − wg + Gs ) · Dg + (pn − wn ) · Dn + (pm − wm ) · Dm ⎪ ⎨if 0 ≤ θ < 1 m πR (θm ) = ⎪(pg − wg + Gs ) · Dg + (pn − wn ) · Dn + (pm − wm + Gs ) · Dm ⎪ ⎪ ⎩ if θm = 1. (9) To determine the equilibrium solution of the game and, hence, the optimal value θm , we have to identify which of those cases leads to a higher profit for the mixed manufacturer. Furthermore, if the interior optimal solution for θm lies outside the variable’s domain, boundary solutions must be considered, as shown in the following section.

4

Solution Approach

This section presents a solution approach for the previously described model. As mentioned before, it is necessary to make a case distinction, i.e., a division into an interior solution (Case I) and boundary solutions (Cases II and III). First of all, it must be noted that the interior solution of the model may result in values outside the definition range of θm . If θm < 0, θm must be rounded up to 0 (Case II). Otherwise, if θm ≥ 1, then the value must be rounded down to θm = 1 (Case III). Moreover, in Case I, the mixed manufacturer’s profit function is not concave for all parameter combinations. In such a case, the optimal value for θm is again a boundary solution (Case II or III). However, the case θm = 1 (Case III) must also be compared with the interior solution in general, as it may be possible that the mixed manufacturer is better off with the maximum greening level than with the interior solution due to the retailer’s subsidies. In the following, we derive the optimal variable values for each case and then present the procedure for determining the optimal solution of the entire model. Case I: Interior solution We apply backward induction to determine the players’ optimal strategies and

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the equilibrium. The retailer acts as the Stackelberg follower and determines its optimal retail prices pg , pn , and pm for each of the hydrogen types sold, given any decisions made by the corresponding manufacturers: max πR = (pg − wg + Gs ) · Dg + (pn − wn ) · Dn + (pm − wm ) · Dm

pg ,pn ,pm

(10)

The problem is solved by setting the first order derivatives with respect to pg , pn and pm equal to zero which yields:   ag (b − e) + eam + ean + b2 wg +b2 (−Gs ) − bewg + beGs + bk − 2e2 wg + 2e2 Gs + ekθm − ek (11) pg = 2(b − 2e)(b + e) e [ag + am − an − (b + 2e)wn + kθm ] + b (an + bwn ) + ek pn = (12) 2(b − 2e)(b + e) e [ag − am + an − (b + 2e)wm ] + b (am + bwm ) + k(b − e)θm + ek pm = (13) 2(b − 2e)(b + e) We check the corresponding Hessian matrix to ensure the retailer’s profit function is concave with the decision variables. ⎛ 2 ⎞ ∂ πR ∂ 2 πR ∂ 2 πR ⎛ ⎞ ∂p2g ∂pg ∂pn ∂pg ∂pm −2b 2e 2e ⎜ ∂2π ⎟ 2 2 ∂ πR ∂ πR ⎟ R ⎝ 2e −2b 2e ⎠ H (πR ) = ⎜ (14) ⎝ ∂pn ∂pg ∂p2n ∂pn ∂pm ⎠ = 2 2 2e 2e −2b ∂ πR ∂ πR ∂ 2 πR ∂pm ∂pg ∂pm ∂pn

∂p2m

The first leading principle minor equals to H1 (πR ) = −2b < 0

(15)

which is always negative as 0 < b ≤ 1. The second leading principle minor equals to H2 (πR ) = 4b2 − 4e2 > 0.

(16)

The above condition is fulfilled if b > e, which is a common assumption in literature [4]. The effect of self-price has to be greater than that of cross-price, which means that a price increase on the own product leads to a higher customer outflow than an increase in the customer base due to a price increase on the other product. Therefore, the company’s product is always more affected by a price change than the external product. The third leading principle minor equals H3 (πR ) = det H (πR ) = −8b3 + 24be2 + 16e3 < 0.

(17)

Taking into account that b > e to satisfy Eq. (16), the following must apply so that Eq. (17) is also fulfilled: 0 0. H2 (πM m ) = det H (πM m ) = − b (c1 − Gt ) (bc1 − bGt − 2k) + bη − 4 4 (29) Since it is obvious that πM m is concave in wm , but not necessarily in θm , a boundary solution must be considered for θm if the condition in Eq. (29) is not satisfied. Then, the optimal solution can be obtained either by means of Case II or Case III. Case II: Boundary solution, θm = 0 To obtain the results for the first boundary solution, we set θm = 0 in Eqs. (11)– (13) and (22)–(24). Case III: Boundary solution, θm = 1 Applying a similar solution concept as before, we obtain the following results for case 3. For the complete proof, please see the appendix. pg =

pn =

pm = wg =

wn =

wm =

ag (b − e) + e (am + an + (b + 2e)Gs ) + (b + e)(b − 2e)wg + b (k − bGs ) 2(b − 2e)(b + e) (30) an (b − e) + eag + eam + (b + e)(b − 2e)wn + 2ek 2(b − 2e)(b + e)   e (ag − am + an + (b + 2e)Gs ) +b (am − bGs + k) + (b + e)(b − 2e)wm 2(b − 2e)(b + e) ag + bc0 + bc1 + bGs − eGs + ewm + ewn + k 2b   2b (an + bc0 ) + eag + 2b2 Gt + bc0 e + bc1 e −3beGs + e(2b + e)wm − e2 Gs + ek 4b2 − e2   2b (am + bc0 + k) + eag − eam + ean + 2b2 c1 +2b2 Gs + bc0 e − 2beGs + beGt − 2e2 Gs . 2(b − e)(2b + e)

(31)

(32) (33)

(34)

(35)

Solution procedure: We suggest the following solution procedure to determine the equilibrium solution: Step 1: For the parameter combination at hand, check whether the condition in (29) is satisfied. If so, proceed with Step 2. Otherwise, calculate the value of πM m resulting from the boundary solution of Case II and proceed with Step 4.

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Step 2: Calculate the value of πM m resulting from an interior solution (Case I), using Eqs. (11)–(13) and (22)–(25). Check, whether θm is outside its definition range. If so, proceed with Step 3. Otherwise, proceed with Step 4. Step 3: a) If θm < 0, calculate the value of πM m resulting from the boundary solution of Case II. b) If θm > 1, proceed with Step 4. Step 4: Calculate the value of πM m resulting from Case III, using Eqs. (30– 35). Step 5: Of all calculated solutions, choose the one that leads to the maximum profit πM m as the equilibrium solution.

5

Numerical Example and Sensitivity Analysis

5.1

Basic Example

The following parameter values are used as the basis for the subsequent numerical analysis (Table 3): Table 3. Numerical example Parameters

ag

an

am

b

c0

c1

e

η

k

Gt

Gs

Values

0.3

0.4

0.3

0.4

0.01

0.02

0.1

0.25

0.3

0.02

0.04

Applying the solution procedure suggested in Sect. 4, we obtain the following results, rounded to seven decimals (Table 4): Table 4. Results of decision variables and profits Decision variables

Results

Profits

Results

wg wn wm θm pg pn pm

0.9710628 0.7266184 0.7618841 0.4391304 1.7814010 1.4791787 1.5285507

πR πM g πM n πM m πSC

0.3771160 0.0521198 0.0970554 0.0830264 0.6093177

For this specific numerical example, though being the Stackelberg follower, the retailer makes the highest profit in the SC. However, it should be noted that this depends on the parameter situation and may only sometimes be the case. Besides, it can be seen that the mixed manufacturer chooses a medium greening level. Customers pay the highest price for green hydrogen, followed by mixed hydrogen. The grey hydrogen is the cheapest. Nevertheless, having the highest market potential, the non-green manufacturer makes the highest profit among the manufacturers.

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Sensitivity Analysis

The example above was used as a basis for the numerical analysis in which the parameter values were varied successively within their domain as part of a sensitivity analysis. One aim of our research is to find out in which situations the mixed manufacturer places itself more in the range of a non-green hydrogen manufacturer or the range of a greener hydrogen manufacturer, i.e., how it chooses its greening level. Interestingly, the decisions regarding θm are not continuous but erratic in many cases. A switch from an interior to a boundary solution can occur when the parameters reach a certain threshold. As displayed in Fig. 2, if, e.g., the cost coefficient for greening level improvements η is small (η < 0.2), then the advantages of investing in the greening level predominate to such an extent that the mixed manufacturer decides on the maximum greening level. That is because the retailer then receives subsidies, which allows it to set higher prices without loss of profit. The manufacturers benefit from this behavior and charge higher wholesale prices, resulting in higher profits. On the contrary, if the customers’ sensitivity to the greening level k reaches a specific value (k > 0.36), the mixed manufacturer opts for θm = 1. Here, all players benefit from a double effect of increased demand at a larger k and the subsidies at θm = 1, both of which allow them to charge higher prices, leading to higher profits.

Fig. 2. Greening level θm and profits under variations of η and k

In reality, variations due to uncertainties are to be expected, especially from the greening level-dependent unit costs. From the variation of c1 in Fig. 3, the effect can be observed that above certain thresholds, the minimum possible greening level is preferred, and gray hydrogen is produced. The higher the purchase price, the lower the greening level of the mixed manufacturer. The retailer

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57

and the green manufacturer experience a loss of profit under high production costs, while the mixed manufacturer can compensate for these losses by choosing its greening level appropriately. The gray manufacturer, on the other hand, is hardly affected by the rising greening level-dependent manufacturing costs. Due to the model assumption costs were normalized to values between zero and one, the gray manufacturer was assigned the lowest costs. Its profit even minimally increases since it benefits from the losses of the green manufacturer.

Fig. 3. Decision variables under variations of c1

Furthermore, the government’s measures are, of course, also decisive for positioning the mixed manufacturer. If the government wants to encourage investment in green technologies, it has two options: imposing taxes on non-green hydrogen or promoting green hydrogen through subsidies. Interestingly, as shown in Fig. 4, increasing subsidies first leads to a slight decrease in θm . The reason is that the retailer can lower the prices for its products due to the subsidies, but the green manufacturer then charges a higher price for them. That leads to lower wholesale prices of the non-green and mixed manufacturers and, therefore, lower investments in the greening level. Nevertheless, above a specific value (Gs > 0.14), the subsidies are large enough for the mixed manufacturer to take on the investment costs of the maximum greening level and thereby be in a better position than without subsidies. Above that threshold, the mixed manufacturer’s profit increases with a higher value of Gs . As can be seen, the threshold at which the mixed manufacturer opts for the maximum greening level is reached sooner for subsidies than for taxes (Gt > 0.3). We tested several other parameter combinations and found none in which taxes performed better than subsidies, although this cannot be excluded entirely. Positive incentives seem more likely to lead to investments in green technologies than punitive instruments such as taxes.

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Fig. 4. Greening level θm and profits of the players under variations of Gs and Gt

Fig. 5. Decision variables under variations of an and am

In order to get an impression of the influence of the market potential on the players’ decisions, we vary the market potential of non-green and mixed hydrogen at fixed ag (see Fig. 5). As we assumed ag = 0.3 in our basic example, both an and am can take on values between 0 and 0.7, whereas an = 0.7 − am holds. Again, we can observe that the maximum greening level is chosen above a certain threshold (am > 0.54). A higher market potential leads to higher prices for mixed hydrogen. Interestingly, the decisions of the market whose market potential is kept constant in the sensitivity analysis (here from green hydrogen)

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are relatively constant and show only slight influence by changes in the other market potentials.

6

Conclusion

In this paper, we presented a hydrogen SC model in which three manufacturers compete in selling different types of hydrogen. While a fixed green hydrogen manufacturer and a fixed non-green manufacturer only determine their prices, our investigations focused on how a mixed manufacturer determines its greening level and positions itself on the color scale of hydrogen. Additionally, through production and demand function parameters, the paper investigated governmental interventions like subsidies and taxes to determine their influence on players’ strategies. The main finding of our model is that regarding government instruments, positive measures such as subsidies seem to be more effective than negative measures such as taxes. If the retailer is subsidized, not only the retailer itself but also the manufacturers benefit. The mixed manufacturer is more likely to choose a maximum greening level than with an identical tax rate. Besides, we observed that the mixed manufacturer abruptly decides to go to the maximum greening level above certain thresholds, thus putting itself in an equal position with the green manufacturer. That could be explained by the fact that the retailer only receives subsidies if green hydrogen is sold, i.e., θm = 1. The mixed manufacturer, therefore, chooses the maximum greening level, e.g., at a low-cost coefficient η or a high green sensitivity k. On the other hand, the mixed manufacturer chooses the minimum greening level if, for example, the greening level dependent costs are high. Although these abrupt changes are also caused by the mathematical model formulation, transitions to gray or green hydrogen in a smoother form are also expected in practice. One avenue of future research could be to include the supplier side in the considerations. Zhang and Liu [36], e.g., look at a three-level hydrogen SC with one supplier of resources, one manufacturer, and one retailer, which provides further research opportunities. In practice, the green electricity required for producing green hydrogen, e.g., may not be available in unlimited quantities. The production capacities of the manufacturers could also be limited. As a consequence, customer demand may still need to be fully met. Moreover, our paper did not model the government as an active player. We could gain further interesting insights if the government’s perspective were included. In reality, the subsidies that turn out to be positive in our model imply costs for the government. From a governmental perspective, a balance should be achieved between expenditures and revenues that maximizes the amount of green hydrogen sold.

Appendix Case III: Boundary solution, θm = 1 We set θm = 1 and determine the optimal retail prices pg , pn and pm for each of

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the hydrogen types sold, given any decisions made by the corresponding manufacturers: max πR = (pg − wg + Gs ) · Dg + (pn − wn ) · Dn + (pm − wm + Gs ) · Dm

pg ,pn ,pm

(36) Please recall that the retailer obtains subsidies for selling green hydrogen, i.e., for θm = 1. The problem is solved by setting the first order derivatives with respect to pg , pn and pm equal to zero which yields: pg =

ag (b − e) + e (am + an + (b + 2e)Gs ) + (b + e)(b − 2e)wg + b (k − bGs ) 2(b − 2e)(b + e) (37)

an (b − e) + eag + eam + (b + e)(b − 2e)wn + 2ek 2(b − 2e)(b + e)   e (ag − am + an + (b + 2e)Gs ) +b (am − bGs + k) + (b + e)(b − 2e)wm . = 2(b − 2e)(b + e)

pn =

(38)

pm

(39)

To ensure the retailer’s profit function is concave in the decision variables, we check the corresponding Hessian matrix, which appears to be the same as in Eq. (14). Therefore, the retailer’s profit function is concave for 0 < e < 12 and 2e < b ≤ 1. Next, we analyze the manufacturers’ decisions, who act as Stackelberg leaders and take their decisions simultaneously, thus, resulting in a Nash equilibrium between them. Taking into account the reactions of the retailer, Eqs. (37)–(39), the following optimization problems occur: 1 πM g = (wg − c0 − c1 ) · Dg − η max wg 2 max πM n = (wn − c0 − Gt ) · Dn wn

1 2 max πM m = [wm − c0 − c1 θm − Gt (1 − θm )] · Dm − ηθm 2 s.t.(37) − (39).

wm ,θm

To determine the Nash equilibrium, we have to solve the equation system 0,

∂πM n ∂wn

= 0, and

∂πM m ∂wm

(41) (42)

∂πM g ∂wg

=

= 0. We obtain

ag + bc0 + bc1 + bGs − eGs + ewm + ewn + k 2b   2b (an + bc0 ) + eag + 2b2 Gt + bc0 e + bc1 e −3beGs + e(2b + e)wm − e2 Gs + ek wn = 4b2 − e2   2b (am + bc0 + k) + eag − eam + ean + 2b2 c1 +2b2 Gs + bc0 e − 2beGs + beGt − 2e2 Gs . wm = 2(b − e)(2b + e) wg =

(40)

(43)

(44)

(45)

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To make sure, the profit functions of the manufacturers are each concave, we have to check the second order derivatives, which are ∂ 2 πM g ∂ 2 πM n ∂ 2 πM m = = = −b < 0. 2 ∂wg2 ∂wn2 ∂wm

(46)

As all input parameters are normalized to values between 0 and 1, this condition is always fulfilled.

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Green Hydrogen Supply Chains in Latin America – A Research Approach for Partnership Projects with Europe Silvia Guillen Suarez, Tobias Witt, Nadja Schlauch, and Matthias Klumpp(B) University of Göttingen, Platz Der Göttinger Sieben 3, 37073 Göttingen, Germany [email protected]

Abstract. The transition from fossil to climate-neutral energy sources and supply chains has become a pressing challenge. Green hydrogen is expected to play an important role in the decarbonization of the global economy because of its specific versatility. Given the recent imminent energy shortages in Europe, supply of green hydrogen becomes an even more important topic for European countries. Therefore, an informed search is required for international business partners supporting European energy supply in a carbon-neutral economy setting. This could include for example Latin America with access to hydrogen production, based on renewable energy. This paper outlines the green hydrogen production landscape of Latin America and current partnership projects with Europe. The collaboration between these two regions is interesting and selected for the potential in hydrogen production capacities as well as cultural and political closeness in terms of democratic rule and property right as basic requirements for successful long-term collaborations for energy supply chains. While a large number of European companies are planning to use green hydrogen, few Latin American countries like Argentina and Chile are currently producing it. Both, consumption and production capacities are expected to increase in the next years, making the design of green hydrogen supply chains an eminent topic for Latin America and Europe. For this reason, cooperation projects are deemed beneficial to both, European and Latin-American companies and institutions. In most of the observed partnership projects within this paper, European actors contribute the technology know-how and Latin American actors offer a variety of large-scale clean energy resources for future green hydrogen supply chains. Additionally, an overview of the representation of hydrogen in Latin America compared to Europe in global energy scenario studies is presented, indicating a major research gap in linking the global scenario analysis level with the described specific project and supply chain design and development level. The results obtained for the specific collaboration of Europe with Latin America can be transferred to other global regions and cooperations. Keywords: Green Hydrogen · Latin America · Partnership projects

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 64–81, 2023. https://doi.org/10.1007/978-3-031-38145-4_4

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1 Introduction Green hydrogen is usually produced through electrolysis with electricity from renewable energy sources such as wind, solar, and hydro power (National Grid, 2022). Whereas for example grey hydrogen is created from natural gas or methane, using steam methane reformation but without capturing the greenhouse gases generated in the process. Using green hydrogen as energy carrier is taking an increasingly prominent role in global energy discussions, as part of decarbonization concepts for the global economy (Kakoulaki et al. 2021). Thanks to its attributes, green hydrogen has the potential to become a highly efficient energy carrier with zero or very low carbon dioxide emissions attached throughout the lifecycle. Green hydrogen can be used in a wide range of applications: As an energy source for public transport, logistics, heating, manufacturing, and electricity generation; or as an energy storage for large-scale production of synthetic methane, synthetic liquid fuels, ammonia, and methanol. Green hydrogen plays an important role in the substitution of fossil fuels, which is already happening in many countries around the world. This aligns with international commitments such as the Paris Agreement (United Nations Climate Change, 2016) and contributes to mitigation efforts regarding climate change. Therefore, expanding production and use of green hydrogen can contribute to building sustainable energy supply chains. Hydrogen in general can be classified into different categories depending on its production processes. Brown and black hydrogen is produced from lignite (brown coal) or black coal, which is the opposite of green hydrogen because it is most harmful to the environment. It is mostly used in oil refining and fertilizer production. Blue hydrogen is when natural gas is split into hydrogen and CO2 either by Steam Methane Reforming or Auto Thermal Reforming, but the CO2 is captured and then stored. As the greenhouse gas emissions are captured in this case, this mitigates environmental impacts (National Grid, 2022). Electrolysis is a process in which a direct electric current is used to dissociate a water molecule into its components: oxygen and hydrogen, with the hydrogen part used as green hydrogen in the case of renewable energy used for this process (Energy Sector Management Assistance Program, 2020). On a global level, large-scale green hydrogen production and use has yet to be established. Some pilot projects in Latin America have been started in cooperation with European countries and organizations as principal project partners (International Energy Agency- IEA, 2021a). This can be understood against the background of joint interests of European countries looking out for energy partnerships with democratic countries with high natural potential regarding renewable energy sources – where Latin American countries are a natural match on all accounts. Therefore, an in-depth analysis of such collaboration projects exactly between these two major world regions is of high interest. These projects are based on a win-win strategy which includes not only technology transfer und revenues for both partners, but also a long-term partnership by working together to achieve climate neutrality by 2050 at the latest (European Commission, 2020). Due to the current energy crisis in Europe, the goal of meeting European climate protection targets has been accelerated by building new partnerships with Latin American countries (European Commission, 2022). For instance, Europe is implementing green hydrogen strategies and roadmaps as part of its climate policy, while in Latin America,

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not all countries hold such resources and strategies (International Energy Agency- IEA, 2020). In Latin America, most projects are in the test phase, while others produce green hydrogen for self-consumption, such as within the mining industry in Argentina (International Energy Agency- IEA, 2021a). For example, in an IEA report from 2021, the electrolyzer capacity expansion announced by Latin American countries was valued at 13.6 GW, (International Energy Agency- IEA, 2021), and 22 green hydrogen production plants have already been established in Chile by 2022 (Statista, 2022).1 In the context of current and planned green hydrogen projects in Latin America supported by some European countries such as Germany, UK and France, this paper aims to provide an overview of the panorama of green hydrogen production and use in Latin America, considering some European countries as a principal project partner. Since we have not found any previous reviews on partnerships in green hydrogen production, this paper is the first of its kind analyzing the structure and motivations for such international cooperation using SWOT-analysis and investigates the reasons for their unequal distribution among different Latin-American countries. The results can be used to provide further insights into the design of sustainable hydrogen supply chains in order to mitigate global warming. The remainder of the paper is structured as follows: Sect. 2 includes the methodological procedure for finding relevant partnerships. Section 3 describes the green hydrogen landscape and selected projects in Latin America. Section 4 presents existing green hydrogen cooperation projects between Latin America and Europe. Section 5 states an analysis of green hydrogen partnership projects, based on region background and culture. In Sect. 6, an overview of the representation of hydrogen in current energy scenario studies is presented. The paper concludes with a short outlook in Sect. 7.

2 Methodology In this paper, we choose a review as a type of qualitative method, which includes data collection, selection and analysis. Due to the reason that regarding topic green hydrogen production, reports and publications do have emerged only recently in the Latin American region, the data collection about green hydrogen projects in Latin America was the most challenging part. This is especially in comparison to European countries, where most of the green hydrogen related literature is well organized and accessible. An example of this is a commission of the European Parliament, which develops green hydrogen policies with specific targets and plans, whereas the Latin America region does not have an equivalent. There was no previous research related to this topic found. The data we analyzed includes general information on projects, but usually without quantitative data like technical literature or comparisons like national statistics. General information describes, which Latin American countries have or are planning to sign green hydrogen partnership projects with European countries. The disclosed information usually includes investment costs or the benefits of both parties in terms of sustainable and economic development. 1 However, no statistics on installed capacity for green hydrogen production in Chile could be

identified at this point. Because of differing plant capacities, the number of power plants does not necessarily correlate with the total amount of installed capacity (in GW) per country.

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Quantified material flows or environmental impacts, e.g., how much CO2 will be saved, was usually not disclosed. As described earlier, the objective of the literature review is to provide an overview on the hydrogen production landscape in Latin America and current partnership projects with Europe. This provides the opportunity to use this information as a basis when looking for future quantitative results of these partnerships projects. The data of green hydrogen projects in Latin America was taken from Latin American ministry and government web pages. The inclusion criterion to select projects was that they needed to be green hydrogen partnership projects between Latin America and European countries, mentioning the central keyword “green hydrogen” with further combinations as outlined below. The search was implemented between May and October 2022. The data regarding European partnerships with Latin America regarding green hydrogen was taken from energy organizations, European governments, and private companies who are involved in the production of green hydrogen by the way of a Google search with the identical keyword. The implementation was done in the same timeframe. Information was integrated on an individual evaluation basis. The search was implemented with the keywords. • • • •

green hydrogen and partnerships OR projects and Latin America and Europe

using the following academic databases and search engines: Dialnet, BASE Bielefeld Academic Search Engine, Google-Scholar, OSTI.gov, Refseek, and Springer Link. Here, the most difficult part was to find information regarding Latin American green hydrogen production, due to the lack of reliable sources. The criteria for selection of resources was based on the following major factors: • • • •

reliability of information, subject matter, availability of contents and geographic coverage.

The data analysis method subsequently used in this paper after data collection was the SWOT analysis. This paper presents a qualitative research approach for green hydrogen partnership projects between some Latin American and Europe countries, based on the encountered data from both regions. The analysis regarding energy scenarios for Latin America is based on the two most recent World Energy Outlooks by the International Energy Agency (IEA), where hydrogen issues are included in order to link to the first level of analysis.

3 Green Hydrogen Supply Chains in Latin America In February 2021, the Brazilian National Council for Energy Policy established hydrogen as a priority area for R&D resources. Six months later, Brazil’s Energy Research Office released an initial technical document establishing the basis for a national hydrogen

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strategy, preparing guidelines for a National Hydrogen Program (Ministerio de Minas e Energia Brazil, 2021). In Argentina, an inter-ministerial group was created in 2021 to develop a hydrogen roadmap and update the existing hydrogen promotion law. Colombia’s Ministry of Mines and Energy presented its national hydrogen roadmap for public consultation in August 2021. The governments of Bolivia, Costa Rica, El Salvador, Panama, Paraguay, Trinidad and Tobago, and Uruguay are also in the process of developing hydrogen roadmaps and strategy documents (International Energy Agency- IEA, 2021a). In Chile and Peru, the use of low-carbon hydrogen in mining could replace large volumes of diesel and enable significant emission reductions in the long term. Latin American countries could also find opportunities to leverage existing industrial and technological capabilities, value chains and infrastructure and as a part of their long-term low-carbon deployment strategy (Oxford Business Group, 2022). 3.1 Green Hydrogen in Argentina Argentina currently uses about 330,000 tons of grey hydrogen per year. Of those, more than 300,000 tons are used in the petrochemical industry for refineries to lower the sulfur content of diesel fuels. The chemical industry makes up the largest share of the remainder with more than 25,000 tons per year. The hydrogen employed is almost entirely self-produced by these industries, only 2% of produced hydrogen goes to sales. A small share of 0.3% is used for power generation. Some relatively small-scale projects produce green hydrogen. Since 2008, the Hychico plant, in the Argentine Patagonia, has been producing around 52 tons of Hydrogen per year from wind power, using two alkaline water electrolysers with a joint capacity of 0.55 MW. The hydrogen is mixed with natural gas for power generation using a 1.4 MW generation unit that can operate over a wide range of gas/hydrogen blends, including pure hydrogen (International Energy Agency 2021). The Hychico plant is also used to test possibilities for transport and storage of hydrogen. It includes a pipeline of roughly 15 km towards depleted gas wells as test sites for storage (Öko-Institut e.V, 2022). By the year 2030, the Fortescue Metals Group Ltd, Australia, is planning an investment of $8.4 billion in the production plant of green hydrogen on an industrial scale at the province Río Negro. This project will also help to create more than 15,000 direct jobs and additional ones indirectly (Reuters Media, 2021). In March 2022, the governor of the Argentinian province Jujuy met in Paris with executives of the French Development Agency (AFD) to address various financing schemes to direct the execution of a green hydrogen generation project, the thermophotovoltaic complex that is being developed jointly with INVAP S.E., an Argentine company, and the 200 MW photovoltaic plant planned in El Pongo (Gobierno de Jujuy, 2022). 3.2 Green Hydrogen in Brazil Brazil currently does not have a hydrogen production plant, but some projects are in progress. For example, the Brazilian steel company CSN is preparing its entry into the green hydrogen segment with the acquisition of a minority stake in the Israeli startup H2Pro. Through the venture capital division CSN Ventures, the company participated in

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a capital injection for a total of US$75 million for H2Pro with other investors Temasek, Horizons Ventures, Breakthrough Energy Ventures, Yara and the steel company ArcelorMittal. The Brazilian subsidiary of the Anglo-Dutch hydrocarbons company Royal Dutch Shell, Shell Brazil and the Port of Açu have signed a Memorandum of Understanding for the development of a green hydrogen pilot plant at the northern region of the state of Rio de Janeiro. The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH launched the e34 million H2Brasil Programme for Green Hydrogen projects on behalf of the German Federal Ministry for Economic Cooperation and Development and the Ministry of Economics and Climate Change (Niras, 2022). The low production costs in combination with Brazil’s geological and climatic conditions make the country a promising location for the production of green hydrogen (Chantre et al. 2022). 3.3 Green Hydrogen in Chile Among the countries with green hydrogen projects in Latin America, Chile seeks to position itself as a leader in green hydrogen production worldwide, with the announcement of a series of projects in the north and south of the country promoted by Enel Green Power, Linde, Engie, Air Liquide, GNL Quintero, and CAP, which will attract investments of US$1,000 million. Significant volumes of renewable energy investment already deployed have also made the country attractive to clean energy investors. After reaching its goal to produce 20% of its energy with renewables by 2025 five years early, the country aims to reach 40% of clean power generation by 2030. While 57% of Chile’s power generation currently comes from thermal sources, the country has committed to shutter 1.7GW of coal-fired power by 2024 and completely phase out coal by 2040. At the end of 2021, Chile had 5.3GW of solar and 3.1GW of wind online. This represented 28% of the country’s total installed capacity. Wind generation in Chile surged from 3.2TWh in 2018 to 5.9TWh in 2021, while solar output increased from 5.1TWh to 8TWh. Chile attracted around $20.8 billion of clean energy investment over the past seven years. This is mainly due to its well-structured power sector, that has for example, renewable energy auctions for standardized power purchase agreements (PPAs) denominated in US dollars and the ability for developers to sign bilateral contracts outside the regulated market with large consumers (BloombergNEF, 2022). Hidrógeno Verde Bahía Quintero led by GNL Quintero S.A. contemplates the development, construction, and operation of the first large-scale green hydrogen plant located in the central zone of Chile, in the Valparaíso region. The project will produce 430 tons of green hydrogen per year. H2V CAP project: this project seeks to implement a green hydrogen plant in the Biobío region to produce 1,550 tons of green hydrogen per year and reduce more than 161,000 tons of CO2 per year. Located at 4,500 m above sea level, on Cerro Pabellón in Chile’s Atacama Desert, the microgrid pilot project uses solar energy and a 50 kW PEM electrolyzer to produce 10 tons of green hydrogen per year. Since 2019, the project provides dispatchable renewable electricity to cover the needs of a microgrid serving a community of over 600 technicians working at a geothermal plant (Ministerio de Ciencia,Tecnologia, Conocimiento e Innovacion, Gobierno de Chile, 2021).

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3.4 Green Hydrogen in Colombia The Colombian Hydrogen Roadmap was released in September 2021 and charts the path for hydrogen development, production, and use over the next 30 years. The roadmap guides the country in its commitment to decrease CO2 emissions by 51% by 2030 and its goal of being carbon-neutral by 2050, reducing 2.5 to 3 million tons of CO2 over the next decade. The Ministry of Mines and Energy prepared the roadmap with financial support from the Inter-American Development Bank (IDB). The bank will also finance the development of the regulatory framework for the hydrogen roadmap. Green hydrogen, with a potential of more than 3 GW in La Guajira alone, represents excellent economic and development opportunities for Colombia. Hydrogen can be used in applications such as refining, mobility, and production of agricultural inputs. It is assessed that through hydrogen, Colombia can develop new value chains that would boost the economy and create high-quality employment, mobilizing around US$ 5.5 billion and creating about 15,000 jobs between 2020 and 2030 (Burdack et al. 2022). The country’s first pilot project was launched on March 18, 2022, when the stateowned oil company Ecopetrol installed its first electrolyzer, the only one in the whole country. Three days later, the Toyota Mirai was introduced, a car using green hydrogen produced by Ecopetrol. Ecopetrol produces green hydrogen at its refinery in Cartagena. In addition, as Latin America’s fourth-largest oil producer, Ecopetrol announced plans to focus more on geothermal energy and hydrogen to reduce the country’s dependence on fossil fuels. Interest in Colombia in close cooperation with international players is therefore high. Ecopetrol has selected strategic allies in its plan to boost the development of lowcarbon hydrogen in Colombia, with the Spanish H2B2, the French Total Eren and EDF, the German Siemens Energy, the British Empati, and the Japanese Mitsui. The decision comes after the start-up of the electrolyzer supplied by H2B2, a hydrogen Spanish company, for the Cartagena Refinery, which is already undergoing technological tests to accompany the decarbonization plan in the country (Ministerio de Minas y Energia, Colombia, 2022). 3.5 Green Hydrogen in Mexico Currently, projects related to green hydrogen are under implementation in Mexico. First, the company Dhamma Energy which is developing a solar power plant in Guanajuato. Second, Tarafert-2 is a production facility for green hydrogen based on solar energy and electrolysis. Third, a project in Baja California aims to store hydrogen to generate electricity, developed by the company HDF. These are led by internationally backed companies, but also local companies are taking the initiative in this arena such as Grupo Infra, Protexa, Solensa, and Énestas Raw Materials & Fuels, among the others that are seriously looking into hydrogen. The Mexican company Cemex is using green hydrogen in its industrial plants in Europe, although its production cost is expensive. Cost is obviously one of the main obstacles, but experts suggest that in this decade, the costs of green hydrogen are going to be competitive in comparison to grey hydrogen and natural gas. Once costs drop, the use of hydrogen will increase rapidly (Deutsche Gesellschaft für Internationale Zusammenarbeit, Mexico, 2021).

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4 Green Hydrogen Projects with Latin America and Europe In Europe, the green hydrogen sector has reached the pre-commercialization phase and is ready to play its essential role in decarbonizing the economies. (Hydrogen Europe, 2021) After the COVID-19 pandemic, Europe is now coping with the next crisis regarding its energy supply. Since the beginning of the war between Russia and Ukraine in February 2022, Europe accelerated its transition to low carbon energy. The war in Ukraine has pushed up natural gas prices to the point where green hydrogen is now cheaper than grey hydrogen in Europe, the Middle East, Africa, and China. The cost of green hydrogen in China is currently $3.22/kg, compared to $5.28/kg for grey Hydrogen (Recharge News, 2022). The EU has already announced plans for a e300 million funding package for hydrogen, as well as RE-Power-EU’s Hydrogen Accelerator initiative to unhook the region from its dependence on Russian gas (PV-Magazine, 2022). The following subsections present major green hydrogen projects between Latin American countries and selected European countries. 4.1 Latin America and Germany The Federal Government of Germany has been aware of the potential of hydrogen technology for many years. Between 2006 and 2016, around 700 million euros in funding was approved under the National Innovation Program on Hydrogen and Fuel Cell Technology, and between 2016 and 2026, a total of 1.4 billion euros in funding will be provided. In addition, the Federal Government has made use of the financial resources provided under the Energy Research Program to build an excellent research landscape. Between 2020 and 2023, 310 million euros will be provided under the Energy and Climate Fund for practice-oriented basic research on green hydrogen and there are plans to provide another 200 million euros over this period to strengthen practice-oriented energy research on hydrogen technology (Bundesministerium für Wirtschaft und Klimaschutz, 2022). Germany cannot produce the amount of green hydrogen needed in the coming years domestically. By partnering with countries where there is an abundance of sunshine or wind power, it can meet supply needs and promote German electrolysis technology overseas. Currently, Germany is partnering with Brazil, Chile, Colombia, Mexico, and Uruguay: (i) Brazil: The project H2Brasil - Green Hydrogen in Brazil is funded by the German Federal Ministry for Economic Cooperation and Development and the development agency Deutsche Gesellschaft für Internationale Zusammenarbeit. It aims to establish a thriving green hydrogen economy in the South American country. Brazil has the right climatic conditions and infrastructure required to produce green hydrogen using renewable energy to power electrolysis. Green hydrogen could then be imported to Germany for use in various industries including chemical and food production, steel and cement manufacturing, and transport. Another project called the center of excellence in Green Hydrogen, is working on five regional education and training spaces at the states Ceará, Paraná, Bahia, São Paulo, and Santa Catarina (Grupo de estudios do Sector Eletrico, 2022; Niras, 2022).

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(ii) Chile: The Energy Partnership Chile-Germany became operational in April 2019. Partners are the German Federal Ministry for Economic Affairs and Climate Action (BMWK) and the Chilean Ministry for Energy (ME). The Partnership has a fulltime secretariat in Santiago de Chile. GIZ, the executive body of the partnership, can look back to more than ten years of successful cooperation with the Chilean Ministry of Energy (Bundesministerium für Wirtschaft und Klimaschutz, 2021). Project HyPro Aconcagua: The German company Linde proposes to replace part of the current production of grey hydrogen that they have installed in the Aconcagua oil refinery, located in the Valparaíso region and which belongs to ENAP. This project will generate 3,000 tons of green hydrogen per year (E&M Combustión, 2022). (iii) Colombia: Ecopetrol Group and Siemens Energy from Germany started working with a 50 kilowatts electrolyser and 270 solar panels at the Cartagena refinery. The main objectives of this alliance are to achieve competitiveness of the production cost of low-carbon hydrogen, to structure opportunities of financing and investment, to integrate the supply chain of new technologies, to identify markets in early stages and to potentiate access to renewable energies and accelerate projects’ timetables execution (Hydrogen Center Bavaria (H2.B), 2022). (iv) Mexico: The German-Mexican Energy Partnership is supporting Mexico in expanding its renewable energy sector. It is the central platform for energy policy dialogue between Germany and Mexico and was formed in 2016 as part of the joint efforts to achieve a global energy transition. The German Federal Ministry for Economic Affairs and Energy and the Mexican Secretariat of Energy (SENER) are responsible for the partnership. Federal states, the private sector and experts from the academic community and civil society are also involved. With the support of GIZ, a secretariat with offices in Mexico City and Berlin has been set up to coordinate the partnership. It serves as the first point of contact for stakeholders and as an information platform for all activities in the partnership. The GIZ estimates that with the right incentives, Mexico could have installed over 670MW of electrolysis capacity by 2030. In 2050, national capacity could reach 38.7GW, making Mexico a global green hydrogen giant. To reach this goal, Mexico would require US$15.5 billion in investment. Nevertheless, green hydrogen’s impact on climate might still be its best asset. According to the study Green Hydrogen in Mexico (Deutsche Gesellschaft für Internationale Zusammenarbeit, Mexico, 2021), the introduction of green hydrogen technologies could reduce emissions of more than 40 million tons of CO2 per year by 2050, mostly in the transport sector but also for industrial applications. According to the German-Mexican Energy Partnership, the potential for producing green hydrogen is particularly high in northwest Mexico, where solar power resources are abundant. Public transportation, long-distance freight trucks, heavy industries, PEMEX’s refining and petrochemical sectors, and power production and electricity storage are all identified as significant areas of opportunity, which was commissioned by the Mexico-Germany Energy Alliance (Deutsche Gesellschaft für Internationale Zusammenarbeit. Mexico, 2022). Green hydrogen uses in PEMEX’s downstream sector, including refining and ammonia manufacturing, may be worth $1.3 billion per year by 2030, with 1.5 gigawatts

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of electricity generation capacity driven by green hydrogen by 2050. Mexico’s green hydrogen-powered public transportation system may serve 250,000 busses and 250,000 freight vehicles by 2050, generating 1,750 t/yr of demand and employing 90,000 people. In the absence of federal initiatives, state governments are taking the lead with more aggressive renewable energy and emissions projects, such as Puebla state’s capacity to create 9,850 t/yr of green hydrogen (Energy news, 2021). Puebla’s administration is banking on renewable energy and gas projects to spur job creation and investment in the state, and it aims to attract the country’s first hydrogen investments to help with industry and public transportation (Deutsche Gesellschaft für Internationale Zusammenarbeit, Mexico, 2021). (v) Uruguay: The German company Enertrag is studying the Tambor Green Hydrogen Hub project in the department of Tacurembó, in cooperation with the local energy consultancy SEC Engineering. The project includes 350 MW of installed wind and solar capacity to produce 15,000 T per year of green hydrogen, which could be converted into e-methanol. This could offset around 10% of the methanol produced conventionally from Russian oil at Germany’s largest refinery, which is an important input for the chemical industry. In addition, e-methanol can also be used as an energy carrier. Uruguay could generate revenues of US$2.1 billion per year by 2040 from the sale and export of green hydrogen and derived products, as contemplated in the country’s roadmap. Domestic demand would come from maritime and heavy transport, and fertilizers, while exports will mainly be geared towards demand for ejet fuel, ammonia, and e-methanol. The country hopes that export-oriented projects will accelerate from the year 2026, when it reaches a green hydrogen production of 1 or 2 GW in 3 medium-scale plants and another plant, at least, for export during the period 2026–2030 (Ministerio-industria-energia-mineria Uruguay, 2022). 4.2 Latin America and the United Kingdom (i) Paraguay: The UK company ATOME Energy PLC signed an agreement with the Itaipu Binational Technology Park to invest US$200 million in state-of-the-art green hydrogen and ammonia production facilities (Posso et al. 2022). The agreement aims at large-scale production of green hydrogen and ammonia from clean energy sources. ATOME has also signed a letter of intent with the company Clean Power Hydrogen (CPH) PLC relating to discussing a potential joint venture assembling electrolysers in South America using the technology of CPH. The electrolyser will be located at the industrial premises of Yguazu Cementos in the Villa Hayes district. Being one of the largest cement works in Paraguay, the facility will be the largest hub for hydrogen mobility in the country. The company aims to use the site to provide hydrogen for both passenger and commercial vehicles in the area (Rivarolo et al. 2019). 4.3 Latin America and France (i) Argentina: Due to the winds of Patagonia and the sun of the Northwest area, Argentina has significant potentials in both energy sources, which has piqued the curiosity of foreign firms that have already settled in the country. The French Development Agency, a public financial institution that implements France’s development

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policy, with a view to combatting poverty and promoting sustainable development (Öko-Institut e.V, 2022), has an investment of US$ 126 million for the Jujuy green hydrogen production project. (ii) Chile: HyEx Project: Led by the French Engie, it seeks to generate an industrialscale pilot plant in the Antofagasta region for the generation of 3,200 tons of green hydrogen per year. This green hydrogen will be supplied to Enaex to produce green ammonia to reduce more than 30,000 tons of CO2 per year. The entire plan involves an investment of EUR 1.5 billion by 2025. It will enable Engie to reduce CO2 emissions by 80% from its energy production activities in Chile by 2026 (Engie, 2021). Another project in this region, is at the Antofagasta Mining Energy Renewable, led by the French Air Liquide, that expects to produce 60,000 tons of e-methanol per year from renewable energy (E&M Combustión, 2022). (iii) Colombia: Ecopetrol Colombia signed a strategic alliance with the French company Total Eren and EDF, to develop green hydrogen projects. The objective is to invest US$2,5 million for green hydrogen production for self-consumption by 2025 and by 2030, to initiate green hydrogen exports to Europe (Ecopetrol, 2022). 4.4 Latin America and Italy (i) Chile: Proyecto Faro del Sur: The project includes the installation of 65 state-of-theart wind turbines, will have a capacity of 325 MW, and will require an investment of US$500 million. In addition, it considers a 12,1-km, 33 kV underground transmission line, which will supply renewable energy to the future eFuel plant that HIF Chile hopes to develop to the north of the Cabo Negro industrial zone in Punta Arenas. It will also produce 25,000 tons of green hydrogen per year in the Magallanes region. The green hydrogen is expected to be sold to HIF Chile, a company that will produce ethanol and e-gasoline for export to Europe. It is estimated that upon obtaining the Environmental Qualification Resolution, the wind park construction will take around 24 months. This phase will create an average of 310 jobs, with a peak of 640 jobs. During the operation phase, it will require an average of 34 direct operators. Enel Green Power Chile is the top generator of non-conventional renewable energy in the country, through the operation of a diversified portfolio of technologies, including wind, solar, hydro, and geothermal power. Its portfolio includes 24 plants with a combined installed capacity of over 1.8 GW in clean energy, divided into 14 solar parks, 7 wind parks, 2 hydropower plants, and the first geothermal operation in Latin America, with the Cerro Pabellón project located in the Atacama Desert and Antofagasta Region (E&M Combustión, 2022). (ii) Brazil: Pecem, Ceara Project: The government of the Brazilian state of Ceara is partnering up with the company Enel Italy to study the potential development of green hydrogen projects with a capacity of up to 400 MW of electrolysis in the first phase. Italy’s Enel Green Power is the leading wind and solar operator in Brazil in terms of installed capacity, with 3GW, in addition to 1.3GW of hydroelectric power potency. The country is expected to absorb almost 70% of its investments in Latin America by 2024, with an investment of US$5,7 billion (Energy news, 2022).

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5 Partnership Analysis The main challenge to increase the use of renewable energy sources in Latin America in the future is to understand how energy and development policies have been elaborated by different governments. An explanation for this is the lack of incentive and foresight of the governments and energy industry to increase the use of this type of energy, for example for the electricity generation. Since Latin America has an abundance of fossils energy resources like oil and natural gas, it is in general easier, cheaper, and more technically feasible to keep exploiting those types of energy sources than to invest in the use of renewable energy sources or to establish appropriate renewable energy policies to promote them. Another explanation is that the development of renewable energy sources clash with the interest of powerful players, particularly big oil and gas companies. For example, Venezuela, a country with the biggest reserve of petroleum in Latin America as well as Bolivia and Peru with the biggest reserves of natural gas, are countries that are still expanding the infrastructure to provide petroleum and gas to all of their regions. Therefore, there are few incentives that some governments are ready to adopt with the purpose of promoting the production and use of clean energies in the near future (Jorge Morales Pedraza, 2012). 5.1 Partnership Projects SWOT Analysis The following analysis identify core strengths, weaknesses, opportunities, and threats of the green hydrogen partnership projects between Latin America and Europe to provide information needed for future partnerships projects, and looking for feasible solutions on the current ones (Fig. 1). 5.2 Strengths and Weaknesses Analysis S1/W1–2: The Latin American geographic factor brings a distinctive advantage in comparison with other regions in regards of transport distance (e.g. compared to Australia) and political alignment in some areas (e.g. compared to African countries). However, both partners should establish strategies regarding the protection of the nature, especially ecosystems and rivers. S2–3/W3: Corruption is one of the biggest problems for Latin American countries, and that makes the market unattractive to investors. Green hydrogen partnership contracts should prevent corruption cases by establishing legal clauses regarding anticorruption. S4/W4: Europe clean energy demand can be satisfied in due time, thanks to the clean energy production partnership with Latin America. But at the same time, Latin American countries do currently not offer stable governments rules in many cases, which could bring difficulties for any project in this area of interest. European energy dependency could be critical if European countries do not work in parallel with other regions like for example Australia. S5/W8: The transition to clean energies should be comprehensively controlled, developing new technologies that minimize possible hazards.

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Strengths Latin American countries offer beneficial geographical conditions (BloombergNEF, 2022) Low-cost production in LA countries in comparison to EU countries Knowledge transfer from the EU brings development to LA and benefits the countries in economic, social, cultural, and environmental terms EU clean energy demand can be satisfied in short time (Hydrogen Europe, 2022) Resource savings New jobs for both regions Faster the change to clean technologies of the industry EU countries like Germany are going to reach their independence from Russian gas Strong Financial resources from the EU with private and public investments Opportunities Building new partnerships with other countries in both regions New export revenues for Latin America Environmental sustainability through the increased use of renewable energies Green hydrogen`s production cost will be competitive at the energy market. Contribution to the world environmental sustainability and SDG objectives (United Nations Climate Change, 2016)

1.

2.

3. 4. 5. 6. 7. 8.

Weaknesses Green hydrogen production by hydroelectricity is achievable in limited areas, because the availability of rivers with specific properties is necessary Solar energy is not very concentrated, therefore, a plant for a large-scale hydrogen production could consume significant land Corruption can compromise the implementation of projects LA unstable government rules EU economic crisis Worldwide inflation Green H2 production higher cost H2 is a highly flammable gas and could propose a hazard

Threats Natural disasters leading to project delays 2. Boycott of projects by gas and oil lobbies due to competing economic reasons and interests 3. Impact on flora and fauna at the project locations (destruction, habitat transfer) 4. Potential distrust and rejection of the projects by the native population 1.

Fig. 1. SWOT-Analysis of partnership projects

S6–7-9/W5–6: The worldwide inflation is affecting all countries, some worse than others, and working together in a fair way to find solutions is the only way to achieve goals.

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S8/W7: Currently, the production cost of green hydrogen is higher, but in a short period, this will change, thanks to the development of new technologies in the energy sector. 5.3 Opportunities and Threats Analysis O1/T1: Building partnerships opens new business horizons with other regions, like North America, Asia, and Oceania. However, no country is free from natural disasters. Partnership projects’ contracts should include contingencies regarding budget and time planning strategies. O2–4/T2: Even though the green hydrogen projects realize their environmental and economic benefits, the gas and oil lobbies could impede or boycott the projects due to economic reasons. This constitutes a major threat to green hydrogen projects. O3–5/T3–4: It is known that technology and industrialization bring enormous consequences to the environment, and the production of green hydrogen is no exception. Since decades, the mining sector in Latin American countries have damaged not only people, but also flora and fauna, which is the reason why native people might have an increased tendency to oppose foreign investors and their projects. It is important in order to reach a sustainable environment that multiple actors and stakeholders work together with native people, giving them fair information, and offering participation in the project. In this way, they can feel an important piece on the transition to a climate-neutral society. Altogether, the implications can be summarized as follows: Theoretical impacts include the notion that existing research in energy transition from countries in the northern hemisphere like Europe could be combined with development economics research for countries like in Latin America. If this combination might be used in a productive manner, the identified opportunities and strengths of Latin America in potentially producing a large volume of green hydrogen for the use in European countries might be use to the benefit of both regions. This insight might spark new research streams in the triangle of sustainability, supply chain management and development economics in order to inform political decision-making on a global scale. For the practical business perspective, the following insights might be useful and improve the competitiveness of firms and projects in this regard: (i) Individual projects can contribute towards the reduction of threads and risks like for example by establishing clear structures, processes and rules against corruption. (ii) A second learning for business decision-makers is the adaption to local requirements and customs like for example the inclusion of native people and their interests. This could strengthen the success and performance of joint projects in a straightforward manner as win-win-win-situations could arise between the native population, public authorities as well as private investors in this regard. (iii) Finally, the opportunity of sourcing green hydrogen in Latin America could be followed through by corporate actors on the condition that their sincere interest lies in technology development and truthful reduction of further environmental and societal risks in Latin American countries. Therefore, dedicated supply chain and technology innovation managers could be trained and employed in order to allow for such a technology development in line with corporate as well as society interests.

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This SWOT analysis is complemented by a meta-level energy scenario analysis. This enables to comparative analysis of the outlined project level before and the perspective of global energy scenarios, where a specific alignment gap can be identified.

6 Energy Scenarios This section aims to provide an overview regarding the representation of (green) hydrogen in Latin America compared to Europe in global energy scenario studies. Despite its opportunities, hydrogen has historically had a limited role in influential global energy scenarios. Whilst more recent studies are beginning to include hydrogen, the role it plays in different scenarios is extremely inconsistent (Quarton, 2020). In this analysis, the two most recent energy scenario studies of the IEA are considered: World Energy Outlook 2022 (International Energy Agency, IEA, 2022) and World Energy Outlook 2021 (International Energy Agency, IEA, 2021b). In their World Energy Outlook 2022, the IEA provides an overview of the historic development of the hydrogen demand in petajoule (PJ) for the years 2020 and 2021 as well as a projection of the future demand in 2030 and 2050 in different regions of the world. 1 PJ corresponds to approximately 8400 tons of hydrogen. The authors compare the future hydrogen demand in 2030 and 2050 using two different scenarios: The “Stated Policies Scenario” (STEPS) and the “Announced Pledges Scenario” (APS). Each scenario is based on a different vision of how policy makers might respond to today’s energy supply crisis. In STEPS, the authors explore how the energy system evolves if current policy settings are retained. These include the latest policy measures adopted by governments around the world, such as the Inflation Reduction Act in the United States, but do not assume that aspirational or economy-wide targets are met unless they are backed up with detail on how they are to be achieved. In APS, governments get the benefit of the doubt. In this scenario, their targets are achieved on time and in full, whether they relate to climate change, energy systems, or national pledges in other areas such as energy access. Trends in this scenario reveal the extent of the world’s collective ambition, as it stands today, to tackle climate change and meet other sustainable development goals. While hydrogen demand in Latin America is 453 PJ in STEPS in 2030, it is projected to be at 580 PJ in APS. In addition, hydrogen demand in 2050 Latin America is projected to be 758 PJ in STEPS and 1528 PJ in APS. Comparatively, hydrogen demand in Europe in 2030 is calculated to be 1109 PJ in STEPS and 1746 PJ in APS. In 2050, the projections are even higher with a calculated demand of 1207 PJ in STEPS and 3890 PJ in APS. It can be observed that the development of hydrogen demand in Central and South America is significantly lower than in Europe whereas in STEPS, the demand forecast is much more conservative than in APS. Additionally, the study provides an overview of the capacity of proposed international hydrogen trade projects targeting operation by 2030 by exporter or importer country. The authors state that exports in Latin America are calculated to be at over 3 million tons of hydrogen equivalent capacity whereas in Europe, the export capacity is projected to be lower with a little under 2 million tons of hydrogen equivalent capacity. In addition, imports in Europe are assumed to be over 6 million tons of hydrogen equivalent capacity

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whereas there are no projected imports for Latin America (International Energy Agency 2022). Information on hydrogen in Latin America in the World Energy Outlook 2021 (International Energy Agency 2021) turn out to be less than in the successor study from 2022. The authors give an overview of planned and announced electrolyser installations up until 2030 and their proportion of required additions. They compare the share of required capacity using two scenarios: the “Announced Pledges Scenario” (APS) and the “net Zero Emissions by 2050 Scenario” (NZE). APS assumes that all climate commitments made by governments around the world, including Nationally Determined contributions (NDCs) and longer-term net zero targets, will be met in full and on time whereas NZE sets out a narrow but achievable pathway for the global energy sector to achieve net zero CO2 emissions by 2050 (International Energy Agency 2021). Overall, the role and share of green hydrogen described in the global energy scenarios described does not fully match the project volumes and sizes found in the project analysis before. Therefore, it has to be concluded that there exists a distinctive gap between the operational project level of new green hydrogen initiatives and projects and the assumptions and numbers of such global energy scenarios. As a conclusion regarding energy scenarios it can be expected that the topic of hydrogen is going to increase in relevance in the following years in global energy scenario studies. Energy systems are becoming more technologically diverse, spatially distributed, and temporally variable. Consequently, there is an opportunity for new options, such as hydrogen, to play a role. The exact role these new technologies will have is unclear, and it is the purpose of energy scenarios to help to indicate what this role might be (Quarton, 2020). This is a potential basis for improved resource allocation on a global scale through better informed political and private corporate decision-making regarding energy infrastructures and markets.

7 Conclusion Based on the defined objectives of this paper, an overview of the panorama of green hydrogen production and use in Latin America is provided. This is considering some European countries including Germany, France, Italy and UK as principal project partners, shows a growing number of green hydrogen partnership projects between both regions. On the one hand, the technical potential that Latin American countries offer for green hydrogen production makes it an attractive region for other countries, who are planning to cooperate and invest in green hydrogen projects. On the other hand, a problem that Latin American countries still have to deal with is the lack of interest in clean energies, because most of them have abundant fossil energy reserves like gas and petroleum. In this regard, supply chain and logistics management additionally does face a design and educational challenge in the transition towards clean energy supply chains in a global perspective. Climate neutrality is a quite new topic for the Latin America region, and for most of the developing countries in this region, the economic situation and economic growth and distribution issues dominates the rest of their problems or necessities (OECD, Organization for Economic Cooperation and Development, 2003). Future research is warranted

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regarding green hydrogen supply chain benchmarking due to the high-level importance for a sustainable global energy system and mitigation efforts regarding climate change. As outlined, especially exploring a possible link between project-level information and inputs on the one hand and global energy scenario analyses on the other hand in order to avoid information and development gaps in this regard.

References Aprea, J.L.: Hydrogen energy demonstration plant in Patagonia: Description and safety issues. Int. J. Hydrogen Energy 34, 4684–4691 (2009) BloombergNEF. Climatescope 2022, pp. 41–53 (2022) Bundesministerium für Wirtschaft und Klimaschutz, Signing of declaration of intent to establish German-Chilean task force on hydrogen (2021). https://www.bmwk.de/Redaktion/DE/Presse mitteilungen/2021/06/20210629-absichtserklaerung-gruendung-deutsch-chilenische-taskfo rce-wasserstoff.html Bundesministerium für Wirtschaft und Klimaschutz, German-Brazilian cooperation on green hydrogen (2022). https://www.german-energy-solutions.de/GES/Redaktion/EN/News/2022/ 20220525-h2-cooperation-brazil.html Burdack, A., Duarte-Herrera, L., López-Jiménez, G., Polklas, T., Vasco-Echeverri, O.: Technoeconomic calculation of green hydrogen production and export from Colombia. Int. J. Hydrogen Energy (2022) Chantre, C., et al.: Hydrogen economy development in Brazil: An analysis of stakeholders’ perception. Sustain. Prod. Consumpt. 34, 26–41 (2022) Deutsche Gesellschaft für Internationale Zusammenarbeit, Mexico, 2021. Green Hydrogen in Mexico: towards a decarbonization of the economy Deutsche Gesellschaft für Internationale Zusammenarbeit. Mexico, The German-Mexican Partnership and the Mexican Hydrogen Association announce strategic alliance (2022). https:// www.energypartnership.mx/tr/home/strategic-alliance-with-amh2/ E&M Combustión, S.L, Green Hydrogen Projects in Latin America, recently announced (2022) Ecopetrol, Ecopetrol closes alliances with six international companies to develop hydrogen strategy (2022). https://www.ecopetrol.com.co/wps/portal/Home/en/news/detail/Noticias-2021/int ernational-hydrogen-alliances Energy news, 2021. Study: 22TW of Green hydrogen in Mexico. https://energynews.biz/study22tw-of-green-hydrogen-in-mexico/ Energy news, 2022. ENEL signs Memorandum with Ceara state government for green hydrogen project. https://energynews.biz/enel-signs-memorandum-with-ceara-state-governmentfor-green-hydrogen-project/ Energy Sector Management Assistance Program, Green Hydrogen in Developing Countries (2020). https://openknowledge.worldbank.org/handle/10986/34398 European Commission, Stepping up Europe’s 2030 climate ambition Investing in a climate-neutral future for the benefit of our people (2020) European Commission, EU external energy engagement in a changing world (2022) Gobierno de Jujuy, A.: Morales en París. Jujuy se lanza a la producción de Hidrógeno Verde (2022). https://prensa.jujuy.gob.ar/proyectos/jujuy-se-lanza-la-produccion-hidrogeno-verde-n105871 Grupo de estudios do Sector Eletrico, Engineering Center of Compentences for Green Hydrogen (2022) Hydrogen Center Bavaria (H2.B), Bavarian hydrogen delegation trip to Colombia: Kick-off for intensification of partnership (2022). https://h2.bayern/en/2022/06/30/bavarian-hydrogen-del egation-trip-to-colombia-kick-off-for-intensification-of-partnership/

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Hydrogen Europe, Post COVID-19 and the hydrogen sector (2021). https://hydrogeneurope.eu/ wp-content/uploads/2021/11/Post-COVID-19-for-the-Hydrogen-Sector_fin.pdf Hydrogen Europe, Clean_Hydrogen_Monitor_10–2022_DIGITAL (2022) International Energy Agency- IEA, Latin America’s hydrogen opportunity: from national strategies to regional cooperation. IEA: International Energy Agency (2020) International Energy Agenc, y- I, EA: Hydrogen in Latin America. OECD, From near-term opportunities to large-scale deployment (2021) International Energy Agency, IEA, 2021b. World Energy Outlook (2021b) International Energy Agency, IEA, Could the green hydrogen boom lead to additional renewable capacity by 2026? – Analysis - IEA (2021) International Energy Agency, IEA, 2022. World Energy Outlook (2022) Pedraza, J.M.: The current and future role of renewable energy sources for the production of electricity in Latin America and the Caribbean (2012) Kakoulaki, G., Kougias, I., Taylor, N., Dolci, F., Moya, J., Jäger-Waldau, A.: Green hydrogen in Europe – A regional assessment: Substituting existing production with electrolysis powered by renewables. Energy Convers. Manage. 228, 113649 (2021) de Ciencia, M.: Tecnologia, Conocimiento e Innovacion, Gobierno de Chile. The Chilean Potential for Exporting Renewable Energy, Chile (2021) Ministerio de Minas e Energia Brazil, Resolucion de consejo nacional de politica energetica (2021). https://www.gov.br/mme/pt-br/assuntos/conselhos-e-comites/cnpe/resolucoes-docnpe/arquivos/2021/resolucao-2-cnpe.pdf Ministerio de Minas y Energia, Colombia, Colombias Hydrogen Roadmap (2022) Ministerio-industria-energia-mineria Uruguay, H2_final_19jul22_Digital_EN, 17–43 (2022) National Grid, The hydrogen colour spectrum (2022). https://www.nationalgrid.com/stories/ene rgy-explained/hydrogen-colour-spectrum Niras, Countdown to a Green Hydrogen Economy in 2030. What does Brazil need to do to deliver on its ambitions? (2022). https://www.niras.com/projects/building-a-green-hydrogeneconomy-in-brazil/. (Accessed 3 August 2022) OECD, Organization for Economic Cooperation and Development, Poverty and Climate Change. Reducing the Vulnerability of the Poor through Adaptation, pp. 16–22 (2003) Öko-Institut e.V, Hydrogen-factsheet-Argentina (2022) Oxford Business Group, Green hydrogen and Latin America’s energy transition (2022). https://oxf ordbusinessgroup.com/news/green-hydrogen-and-latin-america-energy-transition. (Accessed 28 November 2022) Posso, F., et al.: Towards the Hydrogen Economy in Paraguay: Green hydrogen production potential and end-uses. Int. J. Hydrogen Energy 47, 30027–30049 (2022) PV-Magazine, Invasion of Ukraine an inadvertent boost for green hydrogen (2022). https://www. pv-magazine.com/2022/03/21/invasion-of-ukraine-an-inadvertent-boost-for-green-hydrogen/ Quarton, The curious case of the conflicting roles of hydrogen in global energy scenarios. Sustainable energy & fuels, vol. 4(1) (2020) Recharge News, Ukraine war. Green hydrogen is now cheaper than grey hydrogen in Europe, Middle east and China (2022). https://www.rechargenews.com/energy-transition/ukraine-wargreen-hydrogen-now-cheaper-than-grey-in-europe-middle-east-and-china-bnef/2-1-1180320 Reuters Media, Argentina, Fortescue unveil $8.4 bln green hydrogen investment plan (2021) Rivarolo, M., Riveros, G., Magistri, L., Massardo, A.F.: Clean hydrogen and ammonia synthesis in paraguay from the Itaipu 14 GW hydroelectric plant. ChemEngineering 3, 87 (2019) Statista, Number of green hydrogen plants by country 2022 | Statista (2022). https://www. statista.com/statistics/1311948/number-of-green-hydrogen-plants-by-country/. (Accessed 28 November 2022) United Nations Climate Change, The Paris Agreement (2016). https://unfccc.int/process-and-mee tings/the-paris-agreement/the-paris-agreement

Designing Pipeline Networks for Carbon Capture and Storage of CO2 -Sources in Germany: An Industry Perspective Anders Bennæs1 , Martin Skogset1 , Tormod Svorkdal1 , Kjetil Fagerholt1 , Lisa Herlicka2 , Frank Meisel2(B) , and Wilfried Rickels3 1 2

Department of Industrial Economics and Technology, Norwegian University of Science and Technology Management, 7491 Trondheim, Norway Faculty of Business, Economics and Social Sciences, Kiel University, Olshausenstr. 40, 24098 Kiel, Germany {lisa.herlicka,meisel}@bwl.uni-kiel.de 3 Global Commons and Climate Policy, Kiel Institute for the World Economy, Kiellinie 66, 24105 Kiel, Germany

Abstract. In order to reach the two-degree target set by the Paris Agreement and to avoid rising costs due to CO2 allowances and taxes, CO2 intensive industry sectors like cement, steel and chemicals may opt for Carbon Capture and Storage (CCS) solutions. CCS involves capturing CO2 emissions at the source points, transporting it to geological storage sites and storing it their permanently. In this context, our study investigates how to design CCS-pipeline networks that connect German cement, steel and organic chemical industries to the geological storage formations provided by the Longship project, Norway. We propose a mixed-integer programming model for the design of a corresponding onand offshore pipeline network, where seaports serve as intermediate compressor stations for the offshore pipelines. Our results show that the supply chain costs vary significantly across industries due to differences in capture costs, CO2 volumes and the spatial distribution of the point sources. The supply chain costs range from 49.3 Euro per tonne for the organic chemical industry to 83.0 Euro for the steel industry and 108.7 Euro for the cement industry, respectively. With the anticipated increase in the carbon prices in the coming years, CCS might soon become economically desirable for all these industry sectors.

1

Introduction

In order to limit global warming to 1.5 ◦ C Celcius, greenhouse gas emissions must not just be eliminated completely but CO2 must even be removed from theatmosphere to substantial extent. Carbon capture and storage (CCS) refers

c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 82–98, 2023. https://doi.org/10.1007/978-3-031-38145-4_5

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to one of the corresponding technologies. Several pilot projects have already proven the potential application of CCS. For example, CO2 is captured at the natural gas stream of the Sleipner East formation in Norway and stored under the seabed in a geological formation already since 1996 (Furre et al. 2017). Thereby, CCS can be applied to different CO2 sources be it from the energy industry or other sectors. However, the relevance of CCS differs from one sector to another, especially due to different alternative abatement options. For example, industries in the energy sector can likely substitute their CO2 -intensive fossil energy carriers with alternative renewable energies, whereas industries like cement or organic chemicals have fewer alternative abatement options (European Commission, 2018). The industry sector emitted 9.4 Gt CO2 from non-combustion processes worldwide in 2021. The biggest sources were the steel industry (2.7 GtCO2 ), the cement industry (2.52 GtCO2 ), and the chemical and petrochemical industry (1.37 GtCO2 ) in 2021 (International Energy Agency, 2022). The majority of CO2 produced in the steel industry comes from the process of melting iron ore and coke in a blasting furnace (Birat et al. 2004). While green hydrogen may be one mean of decarbonizing the steel industry, CCS is a further option for this sector, especially if green hydrogen is not yet available to the required extent (International Energy Agency, 2019). For the cement industry, CO2 emissions are resulting to a large extent from the need to heat limestone to above 800◦ C, which causes CO2 in the stone to be released. These emissions are hard to abate, and CCS is so far the most promising option for decarbonizing the cement industry (Paltsev et al. 2021). Furthermore, CCS should be the most important lever for CO2 abatement in the chemical industry according to International Energy Agency (2019)’s Cleaner Technology Scenario. Around half of its CO2 emissions are due to the production of chemicals like ammonia where fossil fuels serve as feedstock. In several chemical processes, this share can be reduced by using biomass or green hydrogen as feedstock, but CO2 emissions will still be present to a substantial extent. Thus, CCS appears as an appropriate technology to avoid emissions from the organic chemicals industry too (International Energy Agency, 2019). Several ongoing projects are already testing the maturity of CCS technology for different industries. The oldest projects are capturing CO2 from natural gas processing in the Sleipner and Snøhvit gas fields, Norway, since the 1990 s s and mid-2000s (Holz et al. 2021). Newer projects are under development that consider more diverse emission sources like the production of cement (Heidelberg Materials, 2022) or steel (Global CCS Institute, 2022). In total, there are 196 commercial CCS facilities in operation or planned with a total capture capacity of 42.5 megatons per annum (Mtpa). The trend goes towards collaborative open CCS networks, where CO2 capture projects share the transportation and storage infrastructure to achieve economies of scale. Examples of such networks are the Longship project (1.5-5 Mtpa) and the German EnergyHub Wilhelmshaven (Global CCS Institute, 2022). Longship is a Norwegian CCS project that includes capturing 0.8 Mtpa CO2 from a cement factory in Norway and a waste-to-energy

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plant in the Oslo-Fjord region and shipping the liquefied CO2 to an onshore terminal placed in Kollsnes on the Norwegian west coast. From there, the liquefied CO2 is transported in offshore pipelines to a storage site under the seabed in the North Sea for permanent storage. The concept of the project is to establish an open and flexible infrastructure for the transportation of CO2 by ship towards Kollsnes (Northern Lights JV DA, 2022). In Wilhelmshaven, Germany, the seaport will be the center for a new hydrogen and bio-fuel cluster with open access to various industry partners. In addition to projects that use captured CO2 for transforming green hydrogen into green methane, the companies Equinor and Wintershall Dea also plan to invest in an inland pipeline network to connect CO2 sources in Germany to the port of Wilhelmshaven. During the ramp-up phase, CO2 will then be transported from there to the Longship storage site. In the long run, offshore pipelines will connect the German mainland with storage sites under the seabed of the North Sea to carry the substantial amounts of captured CO2 (Wintershall Dea, 2022). As neither onshore nor offshore permanent storage of CO2 is so far allowed in Germany, a collaboration between Germany and Norway in terms of a CCS supply chain is a very likely option for the future. Based on the prospects of Longship and the Equinor and Wintershall Dea initiatives, this paper investigates how to design a pipeline network for CCS that connects industrial sources in Germany with Norwegian permanent storage locations. In particular, we focus on the industries of steel, cement, and organic chemicals in Germany and investigate what pipeline networks would be required to connect the major sources in these industries and what the resulting cost per tonne of CO2 will be. For this, we reduce the model of Bennæs et al. (2022a), which had a broader scope in the transportation options by considering ship and pipeline transportation in parallel but did not analyze the CCS supply chain for particular industrial sectors. In doing so, our model constructs onshore and offshore pipeline networks between sources, loading and unloading ports, and geological storage locations. Our computational results then reveal the costs of decarbonizing the German industries when CCS is used as a CO2 removal technology. We provide insights about the required investments in pipeline infrastructure and the relationship of costs and spatial distribution of the industries. The paper is organized as follows. In Sect. 2, we review relevant studies in the field. The problem under investigation is formally described in Sect. 3 and mathematically modelled in Sect. 4. We present the design and results of our computational experiments for the German industry sectors in Sect. 5. Section 6 concludes this paper.

2

Literature Review

Several research papers and policy studies consider industries and energy producers as potential CCS applications (e.g. International Energy Agency (2022), Holz et al. (2021), Intergovernmental Panel on Climate Change (2005)). In this context, industrial sources are heterogeneous in terms of their CO2 stream, their

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capture potential, and the locations of the CO2 sources. In particular, capture costs vary across industries. Leeson et al. (2017) and Intergovernmental Panel on Climate Change (2005) review the literature on the major CO2 emitting industries and examine the key drivers of capture costs among industries. Large-scale CCS deployment is characterized by large investments for the required infrastructure, where studies use different methodologies to determine cost-effective CCS supply chains. In the following, we focus on studies that evaluate costs based on a mixed-integer model, as is the case in our study. Including sources from multiple industry sectors in a supply chain allows for increased capture volumes and economies of scale in infrastructure costs. d’Amore et al. (2021) formulate a corresponding mixed-integer programming model to investigate a cost-effective CCS supply chain for Europe with sources from different industries, such as steel, cement, and refinery. They consider the condition and costs of multiple CO2 streams and provide detailed insight into capture cost. For the transportation of the captured CO2 , the investments depend on the selected mode of transportation and the locations of sources and storage. The preferred onshore transportation mode is pipeline transportation (Global CCS Institute (2021), Zero Emissions Platform (2011)). Pipeline transportation offers large transport capacities but is subject to high capital expenditures (CAPEX) and less flexibility compared to ship transportation (Skagestad et al. 2014). Middleton et al. (2012) propose a mixed-integer programming model for a pipelinebased CCS supply chain. Their model decides on the emissions sources to capture CO2 from, the connecting pipeline network, and the storage infrastructure for coal-fired power plants in the United States. Santibanez-Gonzalez (2017) examines the impact of uncertain carbon price and storage volumes for a future CCS deployment in Brazil. The model makes decisions about the integration of sources and storage sites in the supply chain, as well as on dimensioning a corresponding onshore pipeline network. Elahi et al. (2017) focus on a CCS supply chain in the UK using a multi-stage stochastic optimization model. They investigate the effect of uncertainties of the CO2 price and storage capacities on the total cost of the CCS supply chain. They show that with an increasing carbon price, the onshore pipeline network should be extended to include further CO2 sources. Even though pipelines seem to be the preferred onshore transportation mode for CO2 , Becattini et al. (2022) provide a model to design an infrastructure with multi-modal onshore transportation for the Swiss waste-to-energy industry, where pipeline transportation is found to be costly due to the geographical conditions in Switzerland. The permanent storage options for CO2 are mainly located offshore, where the required infrastructure and investment both depend on the used mode of transportation. Pipeline and ship transportation are feasible modes for thisoffshore CO2 transportation. Ship transportation is flexible in the sense that it can well cover various transport volumes and also various distances between the loading and unloading ports. Studies like Bennæs et al. (2022b) and Bjerketvedt et al. (2022) quantify this flexibility and characterize the advantages of

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ship transportation for small volumes and long distances. For the ramp-up phase, ships are the preferred transportation mode. With increasing volumes, offshore pipeline transportation is again the viable solution for a CCS supply chain. Also, d’Amore et al. (2021) and Bennæs et al. (2022a) consider ships and pipelines as possible offshore transportation modes in their models. For a European and German-Norwegian supply chain, both studies show that ship transportation has a low relevance compared to pipeline if the transportation volumes are high. Table 1. Selected relevant literature for this study. Reference

Sector/ industry

Onshore transportation

Offshore transportation

Region

Coal-fired power plants Santibanez-Gonzalez (2017) Cement industry Industry sector Bjerketvedt et al. (2022)

Pipeline

–

US

Pipeline -

– Ship

Energy & industry sector Waste-to-energy plants Industry sector

Pipeline

Pipeline

Brazil Sweden, Norway UK

Pipeline, barge, truck, rail Pipeline Pipeline Pipeline

Pipeline, ship Pipeline, ship Pipeline, ship

Switzerland

Pipeline

Pipeline

Germany, Norway

Middleton et al. (2012)

Elahi et al. (2017) Becattini et al. (2022) d’Amore et al. (2021) Bennæs et al. (2022a)

Energy & industry sector

This study

Steel, cement & organic chemicals industries

Europe Germany, Norway

Table 1 lists the mentioned relevant papers together with their most relevant features. From this table, it becomes apparent that d’Amore et al. (2021), Elahi et al. (2017) and Bennæs et al. (2022a) are most closely related to our work when it comes to considered sectors, transportation modes and regional perspectives. Our study contrasts these studies by putting a focus on individual industry sectors for the particular case of Germany. This is of high relevance as individual industries might face different regulatory frameworks on Germany’s future path towards net zero emissions. None of the mentioned papers supports this issue. More precisely, our model investigates multi-stage pipeline networks for various industries in Germany that capture CO2 and ship it to storage locations in Norway. Thereby, we assume cooperation among those companies belonging to the same industry as is also assumed in Becattini et al. (2022). We focus explicitly on pipeline transportation as we areinterested in the networks that need to be

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established to transport large CO2 volumes after the ramp-up phase. For this, we combine the concepts of an onshore pipeline network like in Middleton et al. (2012) and an offshore pipeline transportation to the final storage locations like in d’Amore et al. (2021). We then derive the overall CCS cost per tonne of CO2 for the considered industry sectors from Germany through computational experiments.

3

Problem Description

We investigate here the establishment of a pipeline infrastructure that connects industrial sources of CO2 in a multi-stage network to final geological storage sites. As our subsequent case study involves emitters in Germany and storage locations in Norway and because initial CCS supply chains will likely use sea transportation for shipping the initially small volumes of CO2 (Bjerketvedt et al., 2022), we assume that ports in Northern Germany and ports at the Norwegian coast are central parts of such pipeline networks, see Fig. 1. More precisely, the transport network consists of onshore pipelines that connect the emitters in Germany with each other and to the German ports, offshore pipelines that connect the ports in Germany and Norway, and storage pipelines that lead from the Norwegian ports to the final storage locations. To formally express the resulting network design problem, we denote by N E the set of industrial emission sources, by N L and N U the set of loading ports in Germany and unloading ports in Norway, respectively, and by N S the set of permanent storage locations. The pipelines between any two locations can be built in different diameters, where D denotes the set of available candidate pipeline diameters. A pipeline of diameter d ∈ D provides a maximal flow capacity of Fd tonnes of CO2 per year. We assume that emission source i ∈ N E produces Pi tonnes of CO2 per year and that a minimum share O of the total CO2 produced in the network is to be captured and transported to permanent storage.

Fig. 1. Schematic pipeline network with onshore and offshore parts.

The network design problem is about deciding on the number and diameter of pipelines that are to be built between any two nodes in the network together

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with the flow of CO2 through each of these pipelines such as to minimize the total required investment and resulting operational cost. For this, we denote PO PS by CiPL jd , Ci jd , and Ci jd the investment per onshore pipeline, offshore pipeline, and storage pipeline, respectively, where i and j refer to the connected nodes and d to the parameter of the pipeline. These investments are scaled to the length of an assumed planning horizon to align it with the operational cost. We do this by simply dividing the total investment by the horizon length of 20 years. Note that our model can decide to establish more than one pipeline of the same diameter between two nodes i and j. In this case, we assume identical costs for each such pipeline even though there might be decreasing marginal construction costs. However, as this effect cannot be quantified precisely and may also vary (e.g., if the pipelines are laid at different points in time), we abstain from including this into our model, also as this would increase the complexity of the model. Operational cost includes CiVj as the cost per tonne of CO2 flowing through pipelines between nodes i and j. Investment and operational cost for capturing one tonne of CO2 at emission source i ∈ N E are expressed by cost rate CiC . Furthermore, CiS denotes the cost of storing one tonne of CO2 at storage location i ∈ N S . The corresponding decisions are captured by integer variables piLjd for the number of onshore pipelines with diameter d between emission source i and for the number of offshore emission source or loading port j, integer variables pO i jd pipelines with diameter d between loading port i and unloading port j, and integer variables piSjd for the number of storage pipelines with diameter d between unloading port i and storage location j. Furthermore, continuous variables ci indicate the quantity of CO2 that is captured in emission source i per year, and fiLj , fiOj , and fiSj refer to the per-year flow of CO2 through onshore pipelines between emission source i and emission source or loading port j, between loading port i and unloading port j, and between unloading port i and permanent storage location j, respectively. All introduced notation is summarized in Table 2.

4

Model Formulation

We present here the corresponding optimization model for the design of a CO2 pipeline network that connects industrial emission sources to permanent storage locations. The model is a reduced version of the one proposed by Bennæs et al. (2022a). Their study also included ship transportation next to pipeline transportation but did not focus on the industrial sources that are investigated here. As our experiments found out that ships do not play a role in the obtained solutions, we concentrate here on presenting a model that focuses on the design of a pure pipeline network. This model is as follows:

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Table 2. Notation Set

Description

NE NS NL NU NP D

Set Set Set Set Set Set

of of of of of of

industrial emission sources permanent storage locations loading ports unloading ports all ports, N P = N U ∪ N L candidate pipeline diameters

Parameter Amount of CO2 generated at emission source i, in tonnes per year Minimum share of CO2 that is to be captured and transported to permanent storage Maximal flow capacity through a pipeline of diameter d, in tonnes per year Cost per tonne of CO2 captured at emission source i ∈ N E Cost per tonne of CO2 stored permanently at location i ∈ N S Cost per onshore pipeline with diameter d between nodes i and j, scaled to the length of the planning horizon Cost per offshore pipeline with diameter d between nodes i and j, scaled to the length of the CiPO jd planning horizon Cost per storage pipeline with diameter d between nodes i and j, scaled to the length of the CiPS jd planning horizon Cost per tonne of CO2 flowing through pipelines between nodes i and j CiVj Pi O Fd CiC CiS CiPL jd

Decision variables ci piLjd pO i jd piSjd fiLj fiO j fiSj

Continuous, quantity of CO2 that is captured in emission source i during one year Integer, number of onshore pipelines with diameter d between emission source i and emission source or loading port j Integer, number of offshore pipelines with diameter d between loading port i and unloading port j Integer, number of storage pipelines with diameter d between unloading port i and storage location j Continuous, annual flow of CO2 through onshore pipelines between emission source i and emission source or loading port j Continuous, annual flow of CO2 through offshore pipelines between loading port i and unloading port j Continuous, annual flow of CO2 through storage pipelines between unloading port i and permanent storage location j

min → Z =

 i ∈N E

+



CiC ci

(1)





i ∈N E j ∈N E ∪N L d ∈ D

+

  

i ∈N L

+

d∈D

  

i ∈N U

+

j ∈N U

j ∈N S

 

i ∈N U j ∈N S

d∈D

L CiPL jd pi jd +

O CiPO jd pi jd + S CiPS jd pi jd +

CiS fiSj





i ∈N E j ∈N E ∪N L

  i ∈N L

j ∈N U

i ∈N U

j ∈N S

 

CiVj fiLj

(2)

CiVj fiOj

(3)

CiVj fiSj

(4) (5)

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ci ≤ Pi

 j ∈N E \{i }

f jiL + ci = 

j ∈N E



j ∈N L

 

i ∈N L j ∈N U

f jiL = f jiO =

fiOj ≤ fiSj ≤

ci ≥ 0 piLjd pO i jd piSjd fiLj fiOj fiSj

+

(6)

i ∈ NE

(7)

fiOj

i ∈ NL

(8)

fiSj

i ∈ NU

(9)

j ∈N E ∪N L \{i }



j ∈N U



j ∈N S

fiOj ≥ O fiLj ≤

i ∈ NE





fiLj

(10)

Pi

i ∈N E



d∈D



d∈D



d∈D

Fd piLjd

i ∈ N E , j ∈ N E ∪ N L \ {i}

(11)

Fd pO i jd

i ∈ N L, j ∈ N U

(12)

Fd piSjd

i ∈ N U, j ∈ N S

(13)

i ∈ NE

(14)

∈Z

i ∈ N , j ∈ N ∪ N \ {i}, d ∈ D

(15)

∈ Z+

i ∈ N L, j ∈ N U , d ∈ D

(16)

+

∈Z

i ∈ N , j ∈ N ,d ∈ D

(17)

E

E

U

L

S

≥0

E

E

i ∈ N , j ∈ N ∪ N \ {i}

(18)

≥0

i∈N ,j∈N

U

(19)

≥0

i∈N ,j∈N

L

U

S

L

(20)

The objective function involves the total annual cost for establishing and operating the pipeline network. It consists of the following terms: (1) cost for capturing the CO2 at the emission sources, (2) cost for building onshore pipelines among emission sources and the loading ports together with the cost of flowing CO2 , (3) cost for building offshore pipelines from loading ports to unloading ports together with the cost of flowing CO2 , (4) cost for building storage pipelines from the unloading ports to the permanent storage locations together with the cost of flowing CO2 , and (5) cost for bringing the CO2 into the permanent storage. The solutions are restricted by Constraints (6) to (20). Thereby, Constraints (6) ensure that the CO2 captured at an emission source does not exceed the CO2 produced at that source. Constraints (7) constitute the flow balancing at the emission sources. Since a source can be connected to other sources via pipeline, it needs to be ensured that the total inflow from other sources plus the CO2 captured at this site equals the total outflow to other sources and to the loading ports. The flow balancing at loading ports is guaranteed by Constraints (8), from which the total CO2 that arrives from sources via onshore pipelines leaves the

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port via offshore pipelines towards the unloading ports. Similarly, Constraints (9) ensure that all CO2 arriving at an unloading port is sent out to permanent storage locations. Via Constraint (10), the model ensures that the required share O of CO2 is captured at the sources and sent via the offshore pipelines to unloading ports. Furthermore, it needs to be ensured that for each active link in the network, the number and diameter of pipelines provide sufficient capacity for transporting the intended amount of CO2 . This is established for onshore pipelines by Constraints (11), for offshore pipelines by Constraints (12), and for storage pipelines by Constraints (13). Finally, Constraints (14) to (20) define the domains of the decision variables.

5 5.1

Computational Study Case Study Data

Using the proposed model, we conduct a case study for relevant industry sectors in Germany. We consider three scenarios that represent the steel, cement, and organic chemicals (Orgchem) industries in this country. These sectors are deemed particularly reliant on CCS to meet CO2 reduction goals (International Energy Agency, 2019). Each scenario includes all industrial emitters (N E ) within the corresponding sector as listed by German Environment Agency (2018) together with their total annual emission volumes Pi . The scenarios are summarized in Table 3 together with expected industry-specific capture cost per tonne CiC (International Energy Agency, 2021). The corresponding Nomenclature of Economic Activities (NACE) describes the core activities of the emission sources within each scenario. Table 3. Characteristics of the considered industry sectors steel, cement, and organic chemicals (Orgchem). Scenario NACE sector

# Emission sources

Total CO2 emissions

Capture cost per tonne

Steel

13

38.1 Mtpa

e 70

30

19.7 Mtpa

e 90

12

15.8 Mtpa

e 30

Manufacture of basic iron and steel and ferro alloys Cement Manufacture of cement Orgchem Manufacture of other organic basic chemicals

The further parameters of the case study are defined as follows. For the offshore transportation to Norway, we consider Wilhelmshaven as the only loading port (N L ) and Kollsnes as the only unloading port (N U ) as these are considered as the relevant logistics hubs for a CCS ramp up via ship, which also makes them likely participate in all future CCS solutions. Furthermore, Kollsnes is nearby

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the permanent storage location of project Longship (N S ). Clearly, the model becomes less complex from just considering one node for each of these sets, but its complexity persists for the onshore network, which is why our later analysis is centered around this network layer. The pipelines are differentiated by a set D of nine diameters that range from 0.2 m to 1.0 m with individual flow capacity Fd per year, where we assume that the CO2 is transported in super-critical state. Constructing a pipeline of diameter d ∈ D between two locations i and j requires PO an investment of CiPL jd for an onshore pipeline, Ci jd for an offshore pipeline, and CiPS for a storage pipeline per kilometer. Furthermore, there is a variable flow jd cost CiVj per tonne of CO2 that is sent from location i to location j and a constant cost of CiS = 6 Euro per tonne for the injection into the permanent storage. PO PS V As CiPL jd , Ci jd , Ci jd and Ci j are two- or three-dimensional parameter matrices, we cannot present the particular values here. Instead, for further details on the computation of all these parameters and the studies that they were taken from, we refer to Bennæs et al. (2022a). Eventually, we set the minimum share of CO2 that is to be captured from all considered emission sources to O = 97%. We set such a high share as our analysis aims at net zero emissions for the considered industries. Thereby, 100% is probably not achievable for technical reasons, e.g., due to leakage, boil-off effects, or the like. The solutions to the three considered scenarios are obtained by solving the corresponding optimization model with Gurobi v.9.1.2. All computations are run on a Dell PowerEdge R640 with 2 × 2.4 GHz Intel Xeon Gold 5115 CPUs and 96 Gb RAM. All models are solved to optimality within computation times that are negligible considering the strategic nature of the problem. 5.2

Results

Key results of the three obtained CCS solutions are summarized in Table 4, including the identified cost and a summary of the pipeline infrastructure. More precisely, this table shows the total supply chain cost per tonne for the three industries, which ranges from 49.3 Euro to 108.7 Euro. The large spread is mainly due to the significantly different capture costs of these industries, see Table 3. Subtracting this cost as well as the constant injection cost of CiS = 6 Euro per tonne reveals the actual transportation cost per tonne of CO2 . This value is 7.0 Euro for the steel industry, 11.7 Euro for cement, and 13.3 Euro for Orgchem, which corresponds to a relative share of 8.5 %, 11.7 % and 27.0 %, respectively. The spread of this cost is explained by the corresponding network structures for each of those industries, see Figs. 2-4. The figures illustrate only the onshore pipeline network within Germany as the resulting link from Wilhelmshaven to Kollsnes and to the final storage looks identical for all scenarios.

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Table 4. Summary of results for the three industry-specific scenarios. Scenario

Supply chain Transportation cost per tonne cost per tonne

Steel

e 83.0

e 7.0

Cement

e 108.7

e 12.7 e 13.3

27.0

Orgchem e 49.3

Relative transportation cost (%)

# Network segments

Total length of pipelines

8.5

15

1867.3 km

11.7

32

2797.2 km

14

2111.9 km

The solution for the steel scenario is depicted in Fig. 2. It shows that there are only a few yet very large sources, see also Table 3. Even though they form clusters that are located far from each other, the optimal network benefits from substantial flows through a total of 15 network segments with highly-utilized pipelines, which leads to low transportation cost of merely 7.0 Euro per tonne of CO2 . Especially the high number of steel factories in the Rhine-Ruhr area contributes to these low transportation costs in comparison to the other sectors.

Fig. 2. Onshore pipeline network for the steel industry.

Figure 3 shows the onshore network for the cement industry. Here, the transportation costs are 12.7 Euro per tonne, substantially larger than the steel scenario. This is due to lower overall volumes (roughly half of the steel industry) that are furthermore spread over many more sources (30 compared to 13 in the steel industry). This results in many more network segments, which, however, are well composed to consolidate the flows among sources and towards Wilhelmshaven.

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Fig. 3. Onshore pipeline network for the cement industry.

Finally, Fig. 4 shows the network for the chemical industry. This industry has the lowest total emissions but also the fewest sources, see Table 3. Most of the sources are clustered in the Rhine-Ruhr area but there are also two remotely located emission sources. Together, this leads to transportation costs of 13.3 Euro per tonne, which is the highest value among all three considered industries. Our analysis focused on networks for individual industries as these might face differing regulatory frameworks that call for individual solutions. Of course, joint cross-industry networks might also be an option for the future. Such networks have been investigated by Bennæs et al. (2022a), where CO2 sources of various sectors have been included altogether. They then determined transportation cost per tonne of CO2 for scenarios with 5, 20, 50 and 100 Mtpa, no matter what industries these emissions stem from. Figure 5 illustrates these costs as triangles. As expected, the transportation cost per tonne decreases with increasing capture volumes due to economies of scale in the joint networks, where the largest share of the cost reductions realizes between 5 and 20 Mtpa. The figure also shows the individual transportation cost for the three industries considered in our paper (as diamonds). It can be seen that the cement and orgchem industry lie above the regression curve, which is due to their relatively small individual capture volume that is spread over a relatively large number of emission sources. Hence, comparing the transportation cost per tonne of CO2 of these industries to the cost of a joint network with similar capture volume reveals that such industries would indeed benefit from a shared pipeline network. In contrast, the steel industry lies below the regression curve. This means that the steel industry performs better than a joint industry network of similar capture volume due

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Fig. 4. Onshore pipeline network for the orgchem industry.

Fig. 5. Transportation cost per tonne of CO2 for industry-specific and joint crossindustry networks

to its large volume at a few concentrated emission sources. Hence, whether or not an industry benefits from participating in a cross-industry network clearly depends on the volumes and spread of its own emission sources. To summarize, we observe that the networks differ in volumes and densities. Through this, the best possible pipeline network supports achieving economies of scale within each such industry but to different extent, as is reflected by the

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identified transport cost per tonne of CO2 . Joint networks between the industries can reduce the transportation cost per tonne of CO2 even further depending on the participating sources.

6

Conclusion

This paper has presented an optimization model for the design of CCS pipeline networks that support industries in capturing their CO2 emissions and transporting them to final storage locations. We have applied this model individually to three industries from Germany. Our results reveal the CCS cost per tonne of CO2 , where the optimized pipeline networks contribute to minimizing the cost of decarbonizing these industry sectors. Thereby, we observe differences in the transportation cost of the three analyzed CCS solutions with 7.0 Euro per tonne for the steel industry, 12.7 Euro per tonne for the cement industry, and 13.3 Euro per tonne for the chemical industry. The differences result from the various volumes, number of sources and network densities of the considered industries. The results can serve as a guideline when industries commit individually to using CCS for avoiding their CO2 emissions. While our study provides insight into static snapshots of potential CCS pipeline networks, future research should investigate the dynamics of establishing this infrastructure in the course of time, i.e., during the ramp-up of CCS where sources and industries join over time.

References Becattini, V., et al.: Carbon dioxide capture, transport and storage supply chains: Optimal economic and environmental performance of infrastructure rollout. Int. J. Greenhouse Gas Control 117, 103635 (2022) Bennæs, A., et al.: Modeling a Supply Chain for Carbon Capture and Offshore Storage - A German-Norwegian Case Study (2022a). working paper available at https://www.scm.bwl.uni-kiel.de/de/team/m-sc-lisa-herlicka/manuscripts/ bennaes-working-paper, submitted for publication Bennæs, A.: Optimization of a ship-based logistics system for carbon capture and storage. In: de Armas, J., Ramalhinho, H., Voß, S. (eds.) Computational Logistics, pp. 44–59. Springer International Publishing, Cham (2022). https://doi.org/10.1007/ 978-3-031-16579-5 4 Birat, J.P., Hanrot, F., Danloy, G.: CO2 mitigation technologies in the steel industry: a benchmarking study based on process calculation. In: Proceedings of the International Symposium on Light Metals and Metal Matrix Composites COM 2004, pp. 17–25 (2004) Bjerketvedt, V.S., Tomasgard, A., Roussanaly, S.: Deploying a shipping infrastructure to enable carbon capture and storage from norwegian industries. J. Clean. Prod. 333, 129586 (2022) d’Amore, F., Romano, M.C., Bezzo, F.: Optimal design of European supply chains for carbon capture and storage from industrial emission sources including pipe and ship transport. Int. J. Greenhouse Gas Control 109, 103372 (2021)

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Elahi, N., Shah, N., Korre, A., Durucan, S.: Multi-stage stochastic optimisation of a CO2 transport and geological storage in the UK. Energy Proc. 114, 6514–6525 (2017) European Commission A clean planet for all; a european strategic long-term vision for a prosperous, modern, competitive and climate neutral economy (2018). https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX: 52018DC0773&from=EN, (Accessed 12 Oct 2022) Furre, A.K., Eiken, O., Alnes, H., Vevatne, J.N., Kiær, A.F.: 20 years of monitoring CO2-injection at Sleipner. Energy Proc. 114, 3916–3926 (2017) German Environment Agency, German Pollutant Release and Transfer Register (2018). https://www.thru.de/thrude/downloads/?L=3#c1318, (Accessed 17 May 2022) Global CCS Institute, Global Status of CCS 2021 (2021). https://www. globalccsinstitute.com/wp-content/uploads/2021/10/2021-Global-Status-of-CCSReport Global CCS Institute.pdf, (Accessed 12 Jan 2022) Global CCS Institute (2022) Global Status of CCS 2022. https://status22. globalccsinstitute.com/, (Accessed 12 Jan 2022) Heidelberg Materials, Carbon Capture and Storage (CCS) - The Brevik CCS project (2022) . https://www.heidelbergmaterials.com/en/carbon-capture-and-storage-ccs, (Accessed 12 Jan 2022) Holz, F.: A 2050 perspective on the role for carbon capture and storage in the European power system and industry sector. Energy Econom. 104, 105631 (2021) Intergovernmental Panel on Climate Change, IPCC Special Report on Carbon Dioxide Capture and Storage (2005). https://www.ipcc.ch/site/assets/uploads/2018/03/ srccs wholereport-1.pdf, (Accessed 12 Jan 2022) International Energy Agency, Transforming Industry through CCUS (2019). https:// www.iea.org/reports/transforming-industry-through-ccus, (Accessed 12 Jan 2022) International Energy Agency, Is carbon capture too expensive? (2021). https://www. iea.org/commentaries/is-carbon-capture-too-expensive, (Accessed 12 Jan 2022) International Energy Agency, Industry-Sectoral Overview (2022). https://www.iea. org/reports/industry, (Accessed 12 Jan 2022) Leeson, D., Mac Dowell, N., Shah, N., Petit, C., Fennell, P.: A Techno-economic analysis and systematic review of carbon capture and storage (CCS) applied to the iron and steel, cement, oil refining and pulp and paper industries, as well as other high purity sources. Int. J. Greenhouse Gas Control 61, 71–84 (2017) Middleton, R.S., Kuby, M.J., Bielicki, J.M.: Generating candidate networks for optimization: The CO2 capture and storage optimization problem. Comput. Environ. Urban Syst. 36(1), 18–29 (2012) Northern Lights JV DA, Accelerationg decarbonisation (2022). https://norlights.com/, (Accessed 12 Dec 2022) Paltsev, S., Morris, J., Kheshgi, H., Herzog, H.: Hard-to-abate sectors: The role of industrial carbon capture and storage (CCS) in emission mitigation. Appl. Energy 300, 117322 (2021) Santibanez-Gonzalez, E.D.: A modelling approach that combines pricing policies with a carbon capture and storage supply chain network. J. Clean. Prod. 167, 1354–1369 (2017) Skagestad, R., Onarheim, K., Mathisen, A.: Carbon capture and storage (CCS) in industry sectors - focus on nordic countries. Energy Proc. 63, 6611–6622 (2014)

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Wintershall Dea, Wintershall Dea and Equinor partner up for large-scale CCS value chain in the North Sea (2022). https://wintershalldea.no/en/newsroom/wintershalldea-and-equinor-partner-large-scale-ccs-value-chain-north-sea-0, (Accessed 12 Jan 2022) Zero Emissions Platform, The costs of CO2 storage post-demonstration CCS in the EU (2011) . https://www.globalccsinstitute.com/archive/hub/publications/119816/ costs-co2-storage-post-demonstration-ccs-eu.pdf, (Accessed 12 Jan 2022)

Engineering Change Management – An Empirical Study on IT, Processual, and Organizational Requirements Thomas Gollmann1 , Raphaela Gangl2 , and Tim Gruchmann3(B) 1 LMtec Benelux B.V., Amsterdam, Netherlands

[email protected]

2 DPR Draxlmaier Procese de Productie Romania SRL, Timisoara, Romania ,

[email protected]

3 Westcoast University of Applied Sciences, Heide, Germany

[email protected]

Abstract. The implementation of Engineering Change Management (ECM) practices challenges companies as it impacts organizational, processual, and IT levels simultaneously. We analyzed the ECM implementation in a selected company from the medical device industry, answering the research question of which leading practices support their ECM process. We characterized and systemized related practices based on twelve expert interviews. The interviews were conducted based on an interview topic guide, transcribed, and analyzed with the help of qualitative content analysis. Our analysis provides deep insights into strategies for implementing or changing processes, IT systems, and organizational structures. Keywords: Engineering Change Management · CM2 · Qualitative research

1 Introduction To meet their consumers’ needs, companies must embrace technological change through innovation (Maceika and Toloˇcka 2021). Thus, improving existing products, identifying, and eliminating potential problems during product development is necessary. At this point, Engineering Change Management (ECM) refers to the management of changes made directly to the product, often occurring during product development (Jarratt et al. 2011). Technical product changes may be required due to product optimizations, functional enhancements, cost-cutting measures, quality, and safety requirements. These engineering changes (EC) are to be managed as part of a structured process to ensure that the changes are communicated, agreed upon, coordinated, and implemented on schedule (Lashin 2021). ECs may also be caused by political, economic, environmental, or other forces that are often beyond the control of a company or its customers (Wu et al. 2012). In many cases, ECs lead to other changes, which may affect different parts of the company. For this reason, researchers recommend an interdisciplinary approach to managing ECs © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 99–112, 2023. https://doi.org/10.1007/978-3-031-38145-4_6

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(Wu et al. 2012). ECM addresses the general task of managing and guiding the projectbased process of ECs from identification until release for production. It keeps track of relevant and critical business factors with the help of information systems (Arica et al. 2020). Due to this complexity, this research examines the organizational, IT- and process-related requirements that ECM places on companies and investigates how these can be implemented in corporate practice. We accordingly ask the following research question: What are the leading practices for a company supporting an efficient ECM process? This research aims to explore and define practices for implementing technical changes to enable a sustainable ECM process implementation for many companies. To do so, we conducted a qualitative analysis of expert interviews in an Engineer-to-Order (ETO) company that focuses on medical devices with high complexity and low quantities. The company has a project-based structure with multiple engineering changes per project using the ECM standard CM2 (cf. IpX 2021). The interviews revealed practices, recurring obstacles, and conflicting goals during the introduction of change processes and IT systems. This study thus contributes to the increasing knowledge about ECM processes and the related IT infrastructure. In Sect. 2, the literature background of ECM is briefly introduced, including definitions and basic terminology. Next, Sect. 3 explains the applied methodology in more detail, with a particular emphasis on the company and the CM2 standard. The results of the expert interviews are presented in Sect. 4. Finally, Sect. 5 discusses and concludes the findings as well as gives recommendations for a sustainable and efficient ECM process.

2 Literature Background In the literature, there are different definitions of the ECM. Li and Moon (2012) describe the process as the following: “Engineering Change Management (ECM) refers to a collection of procedures, tools, and guidelines for handling modifications and changes to a product item after its configuration is released” (Li and Moon 2012: 863). Therefore, EC is a core component of ECM. Jarratt et al. (2011) describe ECs as follows: “An Engineering Change is an alteration made to parts, drawings or software that have already been released during the product design process. The change can be of any size or type; the change can involve any number of people and take any length of time” (Jarratt et al. 2011: 105–106). Their statement complements that EC refer to the implementation of a change to a product, while ECM refers to the organization and control of this process (Jarratt et al. 2011). Accordingly, ECM includes all processes, tools, guidelines, and the organizational structure. It leads to the systematic handling of ECs to already released components. ECM thereby minimizes the impact on product quality, costs, and time (Maceika and Toloˇcka 2021). To manage product data, Product Data Management (PDM) systems and Product Lifecycle Management (PLM) systems are often integrated, employing computer-aided databases and collaboration systems. Arica et al. (2020) subdivide ECs into emerging and initiating changes. Changes that arise from the product itself often include bug fixes, security, functional changes, as well as product quality changes. However, ECs become more time-critical and costly later in the design process. Such initiated changes are

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improvements, enhancements, and adjustments to the product. They arise from changes in customer requirements and can be initiated by various interfaces, including suppliers, legislators, customers, and internal functions (Arica et al. 2020). The ECM process consists of several phases: (1) Request – IR/CR, (2) Approval CRB, (3) Notification & Execution – CN/CIB, and (4) After Approval – AO. In the first phase, the initiator enters the change request (CR) into the system containing all necessary information concerning the change, for example, the priority and which components are affected (Kocar and Akgunduz 2010). Then, in the “Approval” phase, a team from different departments deals with the change request and reviews the initiator’s proposed solution. This team is often called Engineering Change Board (ECB). Here, the effects of implementing the change are examined, and possible risks are considered. Finally, if the request is approved, the responsible department is informed by a change notice (CN) and receives all the necessary documents for executing the change (Kocar and Akgunduz 2010). Once approved, the application order (AO) is created. The “After Approval Phase” consists of several steps to implement and document the change (Jarratt et al. 2011). Table 1. Key Actors (IpX 2019; IpX 2021) Actors and Terms

Description

Change Specialist (CS)/Change Leader (CL)

Responsible for organizing and managing the change

Change Owner (CO)

Responsible for discussing suitable solutions with the ECA

Audit and Release Analyst (ARA)

Verifies the change Releases the change

Change Review Board (CRB) & Engineering Change Board (ECB)

Team of members from different departments Review the solution Decide if the change is economical and reasonable

Change Implementation Board (CIB)

Prepare the implementation plan Execute and control the change

Change Notice (CN)

Contains all the information required by the responsible department to implement the change

Change Request (CR)/Investigation Request (IR)

Request for a change Usually initiated by an engineer Potential for improvement is seen Has all the necessary information for the change in it

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The roles in ECM form the foundation of the organizational structure. The realization of technical changes affects all departments. Table 1 provides an overview on essential artefacts and roles, giving a description on responsibilities and tasks.

3 Research Design The study aims to get deeper insights into the ECM process of the specific company in the medical device industry. Thus, a qualitative approach was used to study the complex structures of their ECM and allow for intense interactions with informants, drawing on multiple sources of information (Eisenhardt and Graebner 2007). Although the potentials of ECM are evident in the extant literature, how to develop, transfer, and scale these potentials has not been addressed fully in the literature thus far. In addition, the company was chosen due to the consequent implementation of the CM2 standard (IpX 2021). This empirical study focuses on the analysis of expert interviews, specifically on the processual, IT, and organizational conditions that ECM entails. The interview data was triangulated with further data from other qualitative sources, such as the CM2 standard (IpX 2021). The experts operate a well-established ECM process to assess the maturity status and potentials to optimize the ECM process in general. The selection of interviewees was guided by the different roles in the CM2 standard (see Table 2). The interviews were held with an interviewee topic guide (see Appendix), entirely recorded and transcribed. Table 2. Interviewees Interview

Abbreviation

Role

Duration

Interview 1

I1

Technical Engineer

55:56 min

Interview 2

I2

Change Specialist (CS1)/Change Leader (CL)

46:59 min

Interview 3

I3

Change Specialist (CS2)/Change Owner (CO)

39:56 min

Interview 4

I4

Engineering Data Manager (EDM)

50:05 min

Interview 5

I5

Change Specialist (CS1)/Change Leader (CL)

52:49 min

Interview 6

I6

(Business) Application Owner

59:01 min

Interview 7

I7

IT Manager Engineering

55:13 min

Interview 8

I8

Change Specialist (CS 1–3)

52:57 min

Interview 9

I9

Change Specialist (CS)

54:17 min

Interview 10

I10

Team Leader

51:00 min

Interview 11

I11

Engineering Data Manager (EDM)

51:34 min

Interview 12

I12

PLM Manager

54:56 min

Total Duration

626 min

According to Mayring (2022), qualitative content analysis was applied to analyze the interview data. The method was carried out with the software programs f4transkript

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and f4analysis. With the help of the programs, the interviews were transcribed and coded. According to Kuckartz (2022: 102), the category system “started with deductive categories, and the second step is the formation of categories or subcategories on the material.” The following Table 3 shows the main categories and subcodes of the analysis. Table 3. Coding scheme Codes

Sub-Codes

Frequency

Classification

Process

Orientation to Standards such as CM2

47

high

Process Monitoring

31

high

Impact Assessment

29

medium

Gate Reviews

18

medium

Organization

Applications

Traceability of Changes

23

medium

Order/Modification Release

26

medium

Ad-hoc Tasks

14

medium

Fast Tracks

28

medium

Communication

33

high

Meetings

22

medium

Corporate Culture

37

high

Understanding the Relevance

31

high

Common Language

7

low

Previous Structures

34

high

Definition of Responsibilities

32

high

Understanding other Departments

54

high

Trainings

15

medium

ERP System

18

low

PLM System

44

high

Workflow Tool

19

low

Connectivity between the Tools

34

medium

IT Maturity

24

medium

Data Structure

59

high

Process and IT Work Hand in Hand

66

high

IT Challenges

32

medium

According to Yin (2003), quality procedures concerning internal validity, external validity, construct validity, and reliability need to be in place. Regarding internal validity, the transcript coding was performed by two researchers and cross-checked independently. In terms of external validity, comparisons with the literature were conducted. Construct validity was built by collecting data from multiple sources, while reliability

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was achieved by exposing the parallels across multiple sources. Regarding reliability, single steps of the analysis were documented in a detailed manner as well as analyzed with additional quantitative measures such as frequency.

4 Findings Applications that Support the ECM Processes of the Company The company has an ERP system that collects business data, while the PLM system stores the technical data (Technical Product Documentation, TPD). An interface combines both systems to generate a correct change process. “So, combining these two inputs, you can better estimate what’s really needed to perform the changes correctly so that you will update an appropriate assembly and leave out of the, say, effort the assembly which does not work to update anymore” (I6). The PLM tool thereby covers most of the functions needed in the ECM process. “And another big advantage of our change process is that the documentation or TPD is always correct and up to date. And we can always track all the changes in the TPD very easily” (I2). In addition, a Workflow Management Tool is used to track changes and record all workflows, approvals, and various steps (I6). The workflow tool also controls most of the communication. “So, people will have a workflow tool, and you will get exactly spelled out when you need to do what activity” (I4). In sum, however, it is becoming clear from I11 that there are too many tools. Although the tools are relatively wellconnected, the user faces the challenge of getting to know many tools and switching between them. “That costs time and, therefore also, money” (I11). Accordingly, the connectivity between the workflow and the PLM tool must be improved. “Connectivity is key” (I12). It is a critical success factor in implementing a vital IT infrastructure for the ECM Process. The different actors in the ECM process have different perspectives on the IT maturity level. I1, I2, and I3 rate the IT level as high. The IT systems cover the ECM process completely (I3). In contrast, the application owner I6 and the IT manager I7 agree that the systems are behind state-of-the-art applications. Market trends have yet to be noticed, and system customizations have complicated processes. As a result, updates are more costly and delayed (I6). Regarding data structure, the PLM databases are synchronized overnight but do not allow direct changes from one site or database to another (I5). “I think you can automate much more and connect data and then really have a bit of more technical support from your applications” (I4). Accordingly, a more integrated database would be helpful here (I5). Outdated data records should be checked regularly and removed from the system. Key positions were made inescapable to align the IT systems with the required processes. For example, employees can no longer bypass the change approval to avoid too early or unchecked releases. As a result, data quality increased massively. The higher effort at the beginning reduces quality problems in the long run and, therefore, high follow-up costs (I4). “We know we had a stricter process in place, the data quality went up significantly which also amend that any R&D project got far better inputs to start with […] they thought they would be having in R&D doesn’t exist now anymore because

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yes, they have extra burden, but also there on the long run, they do not fight too many quality issues anymore, right?” (I4) Current reporting indicates possible bottlenecks of the company in processing ECRs. The number of open ECRs and the lead time serve as KPIs. In addition, the duration of the sub-steps can also be recorded (I9). In an ideal case, a Business Intelligence (BI) tool should combine the different inputs to process all data in one system (I6). To improve the KPIs, a multi-stage plan is underway that will simplify updates and globalize processes. In addition, adjustments will shorten processes and standardize the way of working with the process. “So, to whomever you talk from whatever location, 9 out of 10 times, they will tell you no, we should strive for global processes and everything on a global scale, because what works here should also work there” (I7). Finally, a centralized database is supposed to simplify the planning of manufacturing processes with multiple BOMs in the long run (I7). I9 also mentions the complexity of merging the databases. I5 mentioned that the data flow from PLM to ERP is only oneway. The problem will be solved by a custom-built productivity tool that can display both data, but the advantage of a centralized database is also seen here. “So that means ECM process, the perfect world is to have it only under one [PLM tool]. Maybe this will not be OK, we still will need some of the side tools or parallel tools, but we would like to do it as much as possible.” (I11) Business Processes Related to the ECM Process of the Company The CM2 concept serves as a basis for the ECM process of the company using similar terms and roles (I2, I9, I10, I11). “I can say it was quite still, quite similar what we have today in [the company], and it was very close to the CM2 methodology” (I11). However, the procurement and R&D departments work differently because the CM2 method is not flexible enough and too complicated (I2, I10). For instance, the detailed documentation leads to clearly defined roles and responsibilities (I7, I9) but also reduces flexibility. “It will make it easier to understand what you need to do if you have these distinguished roles. But you also need somebody who can do it all if they are out of office. Somebody needs to take it over, and that’s why I like the flexibility. And not everybody liked that. Some people want to do just their own piece of cake” (I8). Process monitoring takes place with several applications and systems. “So, we cannot repeat or change stops when the TPD is completely finished. The technical product documentation is finished and then everything is arranged in our [ERP] system. Then the chain is closed” (I2). Process monitoring, however, varies depending on the complexity of the change (I1). In addition, the CL monitors the change throughout the entire process: “This change leader, he really manages the change from the beginning to the end cooperates with the how to say with the people with the change owners. Generally, they investigating they are doing the analysis, they are changing their drawings bill of materials (BOM) and then communicating with the change planners (CP)” (I11). Interviewee 4 explains that there are many dashboards for monitoring and controlling ECs. This allows performance indicators, but also the response rate of suppliers, to be displayed and monitored well (I4). Impact assessment and evaluation are important steps in the ECM process. If the issue is considered small, the solution path is clear, and the change can be implemented quickly (I2). If the issue is larger and the change affects multiple sides, several meetings

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must be held, and it is understood as a project. “But other changes are more like a little project, and you need to involve ten people have meetings. Make a summary of the meetings, attach that to the change request” (I2). Prioritizing individual changes is described as “something that is really down to your experience” (I1), being an important issue for the impact assessment (I9). “So, if you have a lot of changes, we should be able to really choose the proper one and the really the priority ones and these must be implemented to avoid any manufacturing delays, any additional cost effort, and et cetera.” (I11) All information regarding ECs is recorded in the TPD, and the status can be retrieved at any time (I2). Sometimes, however, the “quality of the TPD […] is not as good as it should be,” and thus, the change is rejected by the archiver (I6). To ensure that all activities are recorded, and the change team can track them correctly, joint meetings with all process participants are conducted (I1). “But what I can definitely recommend for such companies really, you should manage your documentation and your [BOM]. It can be too expensive for a small company, but this is the best case. Do it in Team Center, manage your changes, and write the process down” (I11). Interviewee 12 echoes this by saying that sometimes a change process must go quickly, and it is worth having fully comprehensive documentation to fall back on (I12, para. 43). The application release process is the last step within the ECM process. The change can be approved when the CS3 has checked the complete change and the TPD has been archived (I8). However, not everything goes smoothly, as sometimes reworking and rehashing the TPD is needed. Interviewee 6 explains that it can also be seen as a benefit to have someone sitting at the end of the process, checking, accepting, or rejecting (I6). One manager explains that production status release management has been transferred away from R&D to EDM in that sense. This makes it impossible for an engineer from R&D to release something for production (I4). There is the possibility of a fast track within the ECM process; however, this only applies to small changes with low complexity. “So still rigid, I think because it’s really clear with this reason of change you are allowed to do a fast track or not because then you have, if something is safety critical most human safety. Then you really need to get approvals from different stakeholders to do the change.” (I10) Many of the interviewees see possibilities for fast tracks or bypasses. “Definitely, it has fast tracks. It’s flexible enough if you know what you are doing. I like it” (I2). It was found that such fast tracks are used for simple, minor changes where not all approval steps are needed, or high priority changes (I9, I10). “If something has priority, we can do it fast, we can do everything within a week if needed, then everything drops, and we go there” (I10). Nonetheless, the team is in the process such that they can be monitored (I6). “So, when we started to solidify and when we closed all of the doors to bypass processes was a lot of complaints people, and they saw that they can’t actually play their game how they were used to.” (I4) Organizations, Roles, and Structures in the ECM Process of the Company The interviews revealed that meetings are held with internal and external stakeholders such as R&D, logistics, service, and production (I1). Thereby, the Change Control Board (CCB) meeting is the most common meeting within the ECM process to discuss implementation plans (I8). “CP are always in our CCB, so we have […] service, logistics,

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planning and the change owner, change leader in the same meeting” (I10). In addition, there is the Product Improvement Board (PIB) involving the engineering and sourcing departments (I4, I10). “Yeah, we have here every week a PIB meeting for the technical impact of the changes” (I8) and “where the business is sitting and really business deciding” (I11). Finally, the CRB discusses whether the change is technically feasible and the ECR goes on a fast or full track (I10, I9). Interviewee 10 explains that the EDM team is structured globally, i.e., across sites it “follows globally the same sort of process and change management” (I10). Interviewee 4 explains the process of global team building. Since each site has the same process, it made sense to bring the teams together (I4). “So, if you see it, just do it. […] So that is really motivated, and my company can do so” (I4). It becomes clear that the company is successful if it can deliver and show a certain level of quality and technology maturity. If this is not the case, then this runs through the departments, and in the end, it affects customer satisfaction (I4). “So, this is I’m saying this is a business, and we have to fight. And only the strongest will win.” (I11). Within the old way of working, there were also CCB and PIB meetings (I2), but the roles had to be changed (I3). Accordingly, the responsibilities are related to the different roles. For example, interviewee 7 is in his role as an IT manager responsible for the division’s PLM software. “I’m not into corporate IT, but I’m divisional IT, as we call that” (I7). Interview 2 has the role of a change leader. “So, change leaders are there to see if the change is technically feasible and what their solution is” (I10). They are equivalent to the CS1. The CS1 must check the technical feasibility of the problem and create the ECR (I10). The CO is responsible for the investigation phase and is also called Change Implementation Leader (CIL) or CS2. The CS3 is the last actor in the process. They release the change for production and archive them (I3). Interviewee 11 is a global EDM manager responsible for the overall ECR process at the sites (I11). When implementing the ECM process, frictions between different departments occurred. Interviewee 4 described R&D as a “playground for engineers” and mentioned that some of their freedom was taken away by the solidification of their processes, such that they were hesitant to accept and adapt to it. Interviewee 5 believes that R&D will adopt an even more rigid change process if the systems are good enough to provide the necessary data. Interviewee 4 also explains that adopting ECM in the sourcing department was difficult. “So, there it was really, really tough. And then they need because sourcing they have an incentive, of course, to focus on. Uh, mainly a purchase price for finances. Right? And costs of our goods. And the thing, of course, is that ECM is driving up costs. So, they actually have an incentive to ignore them for as long as they can, right?” (I4) Regarding trainings or courses, it was found that both managers and CS completed such. The extent of the courses varied depending on the position and role in the change process (I1, I2, I9). Interviewee 2 states a manager completed the full training on the CM2 way of working at the beginning, and then the team was divided into the CS1, CS2, and CS3 roles, and each completed the training specific to their role (I2). “If you ask me if I can recommend CM2 training, yes I can recommend it because it can open the mind of the people” (I11). When asked if CM2 training or training on the job is more important, Interviewee 2 responded: “Definitely training on the job. Yeah, definitely for

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new guys; it’s good that some people had the CM2 training. Yeah, you definitely need training on the job, especially for the CCB.” (I2).

5 ECM Framework for Practical Implementations Adapting existing frameworks for ECM cannot be done without the three pillars of process, organization, and applications based on the qualitative analysis. Figure 1 shows an overview of the IT applications to execute the ECM process according to VDI 2219.

Fig. 1. Relevant IT Systems for the ECM Process (VDI 2016)

Extending this framework by assigning CM2 roles (organization) and their responsibilities (process) to applications, we show how PLM product data management, core data management and business process management are linked (Fig. 2). Particularly linking CM2 roles and applications, engineering change workflows become visible (e.g., master data changes). In Fig. 2, the relevant IT systems from Fig. 1 were supplemented by the IT

Fig. 2. Extended implementation framework

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systems/databases as discussed in the company, and CM2 roles were merged. The outlined extended implementation framework can be recommended since theories derived from CM2 are successfully applied in practice and have been introduced in several iterations at the company. Interested parties can abstract the advantages and disadvantages from the results and findings and plan their future step-ups in the field of ECM with more foresight.

6 Discussion and Conclusion The management of the company provides insights into the maturity of ECM processes and tools, to identify which aspects can support further digital innovation. As the company has modeled the EC processes almost entirely on CM2, it is considered a valuable opportunity to study a real-world implementation and related managerial contributions. Thereby, this study connected organizational, processual, and application aspects of ECM to an extended framework. It shows that ECM roles are active in the entire product life cycle but work in different applications. In operation, the framework suggests that ECM is equivalent to PLM core data management in almost all applications. It is, however, not clear whether the entire functionality or add-ons or coded features are used in the PLM tool or other applications. If companies do not have an end-to-end PLM system, they operate ECM core activities in several systems. Since the first step is often to introduce an application, e.g., the Teamcenter Change Manager module, many companies think they have sufficient functionality and governance to support ECM on the system side. Similarly, ECM based on master data governance covers the same areas as the PLM applications and is only supported in other domains with workflow systems. Applications A key finding from the interviews is that the interfaces between the applications must be well connected. Often, fewer tools provide a higher added value instead of introducing many specialized tools with many different functions. Regarding databases, it also became apparent that a uniform data structure is essential for a global company. Individual databases that may need to be adequately synchronized help the efficiency of the process. At the company, the secure database at the end of the process exists, but one database is currently used per location, which is synchronized overnight (I5). Some tools have separate databases to access specific data, but this is necessary because an ERP system works with different data than a PLM system. Therefore, a connection between the two systems is essential and needed (Lashin 2021). Accordingly, the functions of the ERP and PLM systems overlap (Eigner and Stelze 2009) in the company under investigation (I6). Therefore, at these points of conflict, it must always be decided in the application strategy which application should be used for which task. Another point for practice is that one central database is better than multiple databases. Multiple databases must be synchronized and cannot be changed from each location (I5). The same applies to the number of tools to be used. Introducing a new tool for each task increases complexity (I11). A further important point from the perspective of the IT is to avoid customizations of the leading applications to a large extend. Very

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specialized applications increase complexity, make updates more challenging, increase costs and create additional competitive disadvantages (I7). Process In the impact assessment, changes are examined by type and size by CL and the users. For small/simple changes, a fast track may be used. For complex changes, the full track includes the CRB and CCB/CIB meetings where all involved departments are represented (I2, I3). This approach differs from the theoretical model of CM2. After the impact assessment, the CRB discusses whether a fast-track ECR or full-track ECR should be conducted. It is evident that technical changes are only carried out on components released for production. No technical changes are commissioned on products at prototype status (I10, I2). This meets the definition of Jarratt et al. (2011: 105–106), who define a technical change as “a change to parts, drawings or software that has already been released during the product development process.” According to IpX (2019), fast tracks affect about 75–85% of the total changes. Organization From the results of the qualitative content analysis, it becomes evident that the company under investigation has implemented a CM2-like ECM process (I11). However, the distribution of roles, artefacts, and the order of the individual steps is slightly different compared to the CM2 model (I11, I10, I2, I9). Within the analyzed company, it was found that CS1 is responsible for creating the CR (I10) while CS2 is responsible for implementing the CO (I10). At the end of the process, the change is reviewed, released, and archived by the CS3 (I3). Notably, some employees perform multiple roles within the ECM process (I8, I12), and the role description needs to be more consistent. It becomes clear that the precise definition of responsibilities plays an important role. Nevertheless, it seems helpful to have so-called “jumpers” or “supporters” who can take on different roles. The company completely switched to digital communication channels during the Corona pandemic. Since the EDM team is global anyway, online meetings have been held. These included the CRB, the CCB (also called CIB), and the PIB. Within the CRB, the technical feasibility is discussed. A discussion with diverse stakeholders of the change is held in the CCB/CIB meeting. There, the implementation plan is drafted (I8). Within the PIB, the technical impact of the change is discussed. Within the theoretical concept of the CM2 framework, the same boards exist with similar tasks. Maintaining consistent communication in uniform language accordingly leads to fewer misunderstandings and increases process efficiency. Especially for smaller companies, CRB and CIB meetings are more accessible because fewer people are involved (I10). Limitations and Future Prospects The findings of this investigation are representative only for this specific branch of the company. In addition, interviewing twelve experts is conditionally representative of the company. Still, it does contribute to research practice due to the scope of a qualitative study method. In addition, the lack of research on ECMs in practice (Wu et al. 2012) makes it challenging to integrate and compare the findings of this study. Based on this,

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future research may include several series of interviews to be conducted in other sectors to increase representativeness and allow for comparison on a meta-level. Furthermore, in this work, the CM2 concept is regarded as a set of rules for a holistically functioning and efficient ECM. However, due to the non-publicly accessible data on which the model was created, the quality of the framework cannot be clearly assessed. Therefore, another recommendation would be to make the CM2 data from IpX (2019) accessible for research. In doing so, future research may extend the knowledge through cross-sector analysis to further explore and compare different industries. Another way to generate knowledge about the ECM would be a quantitative survey with experts in this field from different companies. For example, Jarratt et al. (2011) have already done research in the automotive sector, but generally on the ECM process rather than specifically on the IT structure. Another limitation is the size of the company. Here, a global corporation with various international locations was researched. There are possible deviations and other necessities for small and medium-sized companies. In addition, this company operates out of industrial locations, perhaps resulting in different priorities for a company located in middle-income or low-income countries. Summarizing future research avenues, the following recommendations for action can be identified through: • • • •

Conducting expert interviews in other industries (cross-sector analysis) Involving small and medium-sized enterprises and possibly start-ups. Researching the ECM process in non-industrialized countries. Conducting a quantitative survey of ECM experts from many industries and companies of different sizes. • Investigating on IT vendor market offering for ECM supporting tools and integrations into PLM-, ERP- and MES-systems • Examining the balance between standardization and customization • Enhancing the ECM framework towards ECM-focused IT-landscapes Acknowledgements. The authors like to express their gratitude to Jan Klindworth and Manuel von Roden from University of Kassel for supporting the data collection and analysis.

Appendix Interview Topic Guide 1. What is your role in relation to the ECM responsible persons in your company? 2. What is typical conflict of objectives/problem, which lead to resistance to redefining the ECM process? 3. What are typical challenges companies are facing in order to involve the various process participants accordingly? 4. How do you structure the process delimitation and fundamental cooperation between different areas of the company? 5. How mature is your organization to deal with change processes? How high is the appropriate IT maturity level and how do you measure it? 6. In which areas of the organization is it easier to implement a successful change process?

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7. What are other important influencing and success factors for the successful implementation of an ECM process? 8. In addition to the top-down process specifications, are there also bottom-up processes that can be influenced in order to influence the ECM process? 9. How sustainable is your ECM process? Is this very rigid or are “back doors”/fast tracks deliberately built in? 10. Are there any other important points you would like to mention? Please reflect on the interview. What is your feedback to the interviewers?

References Arica, E., Bakaas, O., Sriram, P.K.: A Taxonomy for Engineering Change Management in Complex ETO Firms. IEEE Conference Publication (2020) Eigner, M., Stelzer, R.H.: Product Lifecycle Management: Ein Leitfaden für Product Development und Life Cycle Management. Springer, Heidelberg (2009). https://doi.org/10.1007/b93672 Eisenhardt, K.M., Graebner, M.E.: Theory building from cases: opportunities and challenges. Acad. Manag. J. 50(1), 25–32 (2007) IpX: Der CM2 Änderungsprozess - Prozessharmonisierung und Digitales Unternehmen. Kurs CM2-04 (2019) IpX: Das Fundament für Operational Excellence - Die Reise zur Umgestaltung von veralteten Geschäftsprozessen und Softwaresystemen beginnt hier. CM2-01 (2021) Kocar, V., Akgunduz, A.: ADVICE: a virtual environment for engineering change management. Comput. Ind. 61, 15–28 (2010) Kuckartz, U.: Qualitative Inhaltsanalyse. Methoden, Praxis, Computerunterstützung. Juventa Verlag GmbH (2022) Lashin, G.: Technisches Änderungsmanagement. In: Bender, B., Gericke, K. (eds.) Pahl/Beitz Konstruktionslehre, pp. 919–942. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3662-57303-7_20 Li, W., Moon, Y.B.: Modeling and managing engineering changes in a complex product development process. Int. J. Adv. Manuf. Technol. 63, 863–874 (2012) Jarratt, T.A.W., Eckert, C.M., Caldwell, N.H.M., Clarkson, P.J.: Engineering change: an overview and perspective on the literature. Res. Eng. Des. 22(2), 103–124 (2010). Springer Science and Business Media Maceika, A., Toloˇcka, E.: The motivation for engineering change in the industrial company. Bus. Theory Pract. 22(1), 98–108 (2021) Mayring, P.: Qualitative Inhaltsanalyse: Grundlagen und Techniken. In Beltz Weinheim Basel (2022) VDI. Informationsverarbeitung in der Produktentwicklung - Einführung und Betrieb von PDMSystemen. In Beuth Publishing DIN VDI 2219:2016-09 (2016) Wu, W.-H., Fang, L.-C., Lin, T.-H., Yeh, S.-C., Ho, C.-F.: A novel CMII-based engineering change management framework: an example in Taiwan’s motorcycle industry. IEEE Trans. Eng. Manage. Inst. Electr. Electron. Eng. (IEEE) 59(3), 494–505 (2012) Yin, R.K.: Designing case studies. Qual. Res. Methods 5(14), 359–386 (2003)

Transport and Mobility

Urban Mobility and Logistics - Past, Present, and Future Catherine Cleophas(B) and Frank Meisel Kiel University, Institute of Business Management, Kiel, Germany {cleophas,meisel}@bwl.uni-kiel.de

Abstract. This essay reflects on the changes, challenges, and proposed solutions in urban mobility and logistics over time. Thereby, it aims to provide a reference framework for a wide audience of new researchers, researchers lacking a background in transport, and specialists looking for a broader perspective for inspiration. It sheds light on the past, present, and potential future developments. Particular examples are provided for the city of Kiel, Germany. Eventually, we suggest a more holistic research perspective for the logistics management community to contribute successfully to developing future urban mobility and logistics systems.

1

Introduction

Throughout the ages, urban mobility and logistics were very much shaped by the available urban infrastructure, the transport technologies existing at that time, the regulatory frameworks for trade, and the societal and commercial institutions that constituted the rules of daily life. For example, Costa and Fernandes (2012) reflect public transportation in urban areas over the last 250 years from the perspective of technology diffusion. They illustrate the change in vehicles used from carriages and horse-drawn streetcars to early electrified streetcars, and buses with internal combustion engines, revealing that such systems come and go with the technological development and the interest of politics and society in implementing such systems but also disestablishing them when new options become available. When seen from the perspective of transport, urban space separates the starting point and destination of a person’s movement and thus must be overcome as efficiently as possible. Following Aberle (2009), we let the term “mobility” refer to all activities that people conduct outside the home to cover spatial; we consider “logistics” as pertaining to freight mobility. At all times, people have aimed for efficiency in mobility and logistics, be it in terms of cost, time, and also convenience. More recently, environmental sustainability and societal participation have emerged as the main criteria for evaluating urban mobility and logistics systems. Due to these interdependencies, the design and operations of such systems can no longer focus exclusively on efficiency but also need to consider the urban environment and the citizens living there. With the recent mega-trends regarding sustainability, digitization, c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 115–130, 2023. https://doi.org/10.1007/978-3-031-38145-4_7

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customer orientation, platform models, autonomous technology, and many more, future urban mobility and transportation will undoubtedly diverge from the past and present. In this context, this essay combines a historical perspective with a technological, business management, and urban planning perspective to provide a broader understanding of how mobility and logistics emerged throughout time and what options we see for the future. For this, the essay reflects the past (Sect. 2), the present (Sect. 3), and the future (Sect. 4) of mobility and logistics. It also exemplifies potential future developments via two examples from the city of Kiel, Germany (Sect. 5). Finally, we conclude this essay by motivating a more holistic perspective in mobility and logistics research for the future (Sect. 6).

2

Past

Up to about a century ago, most inhabitants of urban areas had to rely on non-motorized transport solutions. The majority of the population covered all distances by foot. Horseback riding, the use of carts, or the use of rowboats and sailboats were other means of transportation available. The resulting comparatively low speeds limited the spatial accessibility of destinations and thus also the extent that an urban area could occupy, especially if, in the context of daily life, both an outward and a return journey had to be made between the starting point and the destination. This limited accessibility went hand in hand with decentralized, small-scale production and supply facilities distributed over an urban area at the time, often accompanied by the unity of the place of work and the place of residence for most of the working population. Notably, since logistics and mobility had to rely on the same means, the distinction seems less clear for the past than for the present and future. The problem of transporting people and goods into and through densely populated urban areas is as old as cities. For thousands of years, goods and raw materials have had to be transported from remote locations to the city itself and then to consumers (Farr, 2014). For example, waterways were available for rapid transportation over long distances in ancient Rome and Egypt. Supplies were then transshipped at city ports for further transport. The motorization that began at the end of the 19th century dramatically changed the mobility framework. With the advent of automobiles, buses, and streetcars, many travellers could cover significantly longer distances in a shorter time. As a result, the transport infrastructure increasingly took over space in urban areas. This was reflected, for example, in the construction of broad, asphalted roads and the establishment of inner-city rail systems. Moreover, as industrialization grew, so did the need for mobility solutions capable of efficiently transporting large numbers of people, especially to move workers between increasingly separate residential areas and production sites on a daily basis. In particular, the reconstruction of Germany’s medium-sized and large cities destroyed during World War II was driven forward according to the paradigm of the “car-friendly city” (Lundin, 2008). The central guiding principle of the

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resulting urban planning and design was the fast and direct accessibility of any place in the urban area by private car. With the onset of the “Wirtschaftswunder” in the 1950 s,s, owning a private car was a direct expression of individual success and prosperity, so the automobile became the preferred means of transport for broad sections of the population. As described for the case of the United States in Jones (2010), the cityscape was extensively shaped by these means of transportation and their associated infrastructures, so “urban space” today is essentially synonymous with “traffic space”.

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Present

As urban areas become more sprawling, crossing distances becomes more challenging for goods and citizens. In urban areas, this combination leads to a conflict between convenience and quality of life, as motorized vehicles contribute to traffic density, noise levels, and air pollution. 3.1

Present: Logistics

Citizens’ tendency to order goods for delivery is increasing too (Sahana et al., 2018; Lone et al. 2021). At the same time, organizing and executing deliveries in urban areas is by no means trivial for vendors and delivery service providers: customers increasingly expect orders to be delivered within the shortest possible time and on time at the desired date, yet delivery should cost as little as possible (Nguyen et al. 2019). Large suppliers are meeting these expectations in an aggressive attempt to gain as high a market share as possible (Jindal et al. 2021). Thus, a conflict of goals emerges, intensifying the tension between the firms’ orientation on financial profit, the customers’ desire for convenience, and the citizens’ quality of life. Service providers often transport freight to urban areas via so-called echelon systems, in which larger bundles of deliveries are brought from outside to central depots and then only transported to the recipient over the “last mile” (Winkenbach et al., 2016). The challenge of splitting up deliveries for further transport within the city, assigning them to means of transportation, and reloading them increases with the size of the cities and the number of deliveries. Figure 1 exemplifies the difference between the transport systems of highly individualized transport between producers, households, and a central marketplace that were predominant in the past (a) and present echelon systems (b). As this figure illustrates, echelon systems support efficient transport for producers outside the city. At the same time, echelon systems may induce longer transport distances between the transhipment facility and households than exchanges between the market and households inside the city. Naturally, this can be avoided by efficiently planning and executing household deliveries through vehicle routing and scheduling.

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Fig. 1. (a) Past: Individual producers (P) and households (H) exchange goods in the urban area around a market (M); (b) Present: An urban consolidation center (UCC) enables transport from producers to households via echelons.

To ensure their profitability, suppliers have to solve various planning problems regarding depots, vehicles, and routes (Olsson et al., 2019): Strategic planning thus seeks to find locations for depots that, on the one hand, are easily accessible by large delivery vehicles from the long haul and, at the same time, allow the shortest possible routes to the addressees. To efficiently transport the deliveries reloaded there to the addressees, operational planning optimizes distribution and routing (Ehmke, 2012). The aim is to use as few vehicles as possible, as additional vehicles and drivers would incur additional costs. Especially when attended home deliveries require the presence of the recipient, e.g., for large appliances, valuable shipments, or fresh food, the deliveries’ convenience comes into focus. In this case, the supplier and recipient must agree on time slots in advance. This further complicates the planning of routes and schedules. One solution may be to offer specific delivery windows only to those customers whose order is particularly worthwhile or who are near an already scheduled trip (Lang et al., 2021). However, this requires close cooperation between the delivery service provider and the seller, as is the case, for example, when the same company offers and implements sales and transport (Zissis et al., 2017). Nevertheless, even if delivery can be profitable for the companies involved and reliably and flexibly implemented for the customer, this still does not make it sustainable for the urban environment and its inhabitants, as lastmile delivery vehicles are predominantly motorized. However, with a growing awareness of the impact of delivery transport on the quality of life in urban areas, this is about to change. Driven by the need to conserve some quality of life for citizens and by calls for sustainability, first steps towards stricter regulatory frameworks governing freight transport are already becoming clear: For example, to move towards fulfilling air quality standards as set by the European Commission, Germany introduced so-called ”low emission zones” (Cyrys et al., 2014), limiting access for certain vehicles. Other cities, such as London, Stockholm, and Singapore, have set charges to limit urban congestion, albeit with limited success (Metz, 2018). Further efforts include providing access licenses (Diao, 2019), restricting access zones (Holman et al., 2015), or limiting access to inner cities for delivery vehicles to certain time windows

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(Reyes-Rubiano et al., 2021). To avoid delivery vehicles choking the limited parking spaces, cities like Paris have established dedicated parking areas for deliveries, causing potentially longer distances that delivery personnel must cross on foot (Dablanc and Beziat, 2015). The use of cargo bikes (Oliveira et al., 2017), which is nowadays already established in many cities around the world, seems to offer an alternative to conventional delivery systems. Cargo bikes present a more sustainable way of delivering goods and circumvent many societal conflicts associated with urban deliveries by (conventional) trucks. Yet, the limited range and capacity per vehicle make this a very labour-intense transportation mode and call for establishing multi-tier distribution systems where goods are first brought to inner-city depots from where they are distributed by bike to the customers. 3.2

Present: Mobility

The concentration on motorized vehicles for transport is increasingly criticized (Jones et al., 2022). Criticism is voiced, on the one hand, of the considerable space requirements of both rolling in the form of generously laid-out roads and stationary in the form of parking spaces, which directly limit the space for alternative forms of mobility, such as bicycle traffic, and thus impair their road safety. This problem motivates practitioners and researchers to look for new transport and logistics options. The considerable public expenditure for constructing and maintaining transport infrastructures is also viewed critically. The limited participation of citizens who do not own cars represents a further potential for social conflict. In particular, the environmental impact of today’s cars, most of which run on fossil fuels, is leading to increasing demand for alternative, emission-free mobility concepts. Politics and urban planners have approached this demand in the past, for example, by setting up environmental zones. Still, from the point of view of large parts of the population, much more effective measures are required, including calls for car-free city centres. In terms of society as a whole, all of this leads to calls for transforming car-oriented cities into liveable cities (Drewes, 2019). Alternatives to motorized individual vehicles (MIVs) are local public transport systems (PTS) in the form of buses, streetcars, or (for urban areas characterized by water) ferries. These transport systems try to cover the primary traffic connections of the urban space by vehicles with many seats on preset routes and regular schedules to provide large transport capacities for this purpose. They represent a central mobility solution, especially for daily commuters and school transport. Cabs, widely available since the beginning of the 20th century, only play a role in occasional transport. In this context, public and private transport are in a classic conflict concerning the population’s acceptance since they differ significantly in cost, time, and convenience. The fact that reaching a destination may require the use of different public transport lines and the alignment of personal mobility needs with the departure times and frequencies of these service systems is generally perceived as a lack of convenience compared to using one’s own car. To be attractive in

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terms of costs, public transport services are usually subsidized to a considerable extent. This enables low fares, which in particular offer participation to population groups that cannot afford their own car or are not in a position to use this means of transport. MIVs are generally considered superior in travel time due to the potentially high speed and lack of commitment to external schedules. However, the massive car traffic in urban areas often creates a different picture; in reality, high traffic loads, especially during rush hour, dramatically reduce the speeds achieved and, in combination with additional time for finding a parking space, etc., significantly increase the total travel time, often to a level that can ultimately even exceed public transport travel times. The “success story” of MIVs has turned into an urban and societal problem of inefficiency due to these burden effects. Despite this, public transport is still frequently regarded as a subordinate mobility option in large parts of society and also in politics, and thus mostly only considered as a secondary factor in urban transport planning. This went as far as dismantling PTS in favour of MIVs. As a result, for today’s cities, public transport and non-motorized alternatives, such as bicycle traffic, have not been at the core of urban and traffic planning until recently. For example, for a long time, cyclists had to “share” the road with private vehicles, and only recently have specially demarcated bicycle lanes increasingly been built in urban areas.

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Future

The future of urban transportation will be shaped by a set of mega-trends as well as numerous emerging technological developments, driving innovative service offerings. In particular, conflicting trends include the tendency towards urban sprawls and periurbanisation versus the drive for agglomeration. Urban sprawl is connected to a country’s increases in wealth, as internal migration drives citizens to move from crowded inner cities and remote rural areas towards suburban spaces but stands in contradiction with sustainable land use and has been shown to increase greenhouse gas emissions (Behnisch et al., 2022). Therefore, advocates of urban sustainability have increasingly called for denser urban areas, what we refer to as agglomeration – examples for the related discussion are given in Surya et al. (2021) for Indonesia and in Butsch et al. (2017) for Pune, India. Another trend is the increased demand for mobility and logistics in developing societies (compare Sun et al. (2022) for an analysis of the ecological impacts of increasing transport infrastructure in BRIC countries) versus the call towards reducing transport to limit its environmental impacts in established “first-world” countries (see, e.g., Sarkis et al. (2020)). As Kuhnimhof et al. (2013) point out, predictions regarding the future demand, particularly for motorized mobility in developing countries, may be feasible based on the historical growth observed in industrialized countries. While, on the one hand, the need for limiting emissions in the climate crisis may set stricter bounds for currently developing countries, on the other hand, arguments based on equity speak against limiting the technological options of developing countries to remedy damages caused by wealthier nations (Shukla, 2019).

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A further societal mega-trend is the ageing of populations observed in many countries around the globe. This development may push the demand for new forms of mobility. In particular, the reduced driving abilities of the elderly may call for the below-discussed autonomous vehicle services; see Kovacs et al. (2020) for a broad review of related research. On the other hand, the COVID crisis has shown that, under pressure, many business meetings in person and the related needs for mobility can be dramatically limited, even creating potential long-term effects on the work routines in globally operating businesses (Jooss et al., 2022). 4.1

Future: Mobility

On the one hand, greater environmental friendliness is made possible by electrifying previously fuel-powered vehicle fleets, as exemplified by the conversion of public transport bus fleets to electric buses (Stumpe et al., 2021). On the other hand, the private sector is increasingly setting up platform-based mobility systems based on the principle of sharing means of transportation instead of owning them, thus opening up broader use and participation in a wide range of services for all parts of an urban population. The goal is to provide flexible and demand-oriented mobility services (Mobility-on-Demand (MoD) and Mobilityas-a-Service (MaaS)). Examples include business models such as that of Uber (2022), in which private individuals offer driving services in their own cars, which customers can conveniently order on demand via an app and, thus, satisfy their mobility needs flexibly in terms of time and location. While this does not initially bring about a fundamental improvement from an environmental perspective, at least from the point of view of coverage, service convenience, and often also price, an improvement in the mobility service can be observed compared to classic cab services and PTS. At present, related systems are still suffering from teething problems: Considerable questions regarding the regulatory framework are not yet answered, for example, concerning working time regulations, billing transparency, and liability. Other sharing services can take, for example, the form of car sharing, bike sharing, and e-scooters, which all contribute to a much more diverse mobility landscape, covering various forms of demand and market segments. These mobility systems can be seen as bridging the gap between MIVs and PTS. Nevertheless, the example of e-scooters shows further potential for conflict. Parking these vehicles anywhere in urban areas and using traffic routes not designed for e-scooters (especially sidewalks) create considerable acceptance problems, especially among those segments of the population that do not use this mode of transportation themselves (Kopplin et al., 2021). For urban planners, the increasing diversity of options creates the challenge of rethinking transportation spaces and developing appropriate transportation infrastructures. One instrument would be to establish so-called mobility hubs, which connect different mobility systems at spatial nodes and thus facilitate the transition from one system to another. Consistent thinking in terms of “mobility chains”, which efficiently connect starting points and destinations via multi-stage

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mobility chains, is one of the core elements here to offer attractive mobility options that make it easier to do without one’s own car. Regarding autonomous vehicles for citizens’ mobility, field tests with autonomous cabs are already taking place in urban areas in the US (Waymo, 2022). The checkerboard-like and explicitly car-oriented urban structures found in many US cities facilitate the use of this technology. Therefore, the widespread use of these vehicles is not expected to induce change in urban spaces and traffic infrastructures. This technology may reduce environmental pollution, primarily if electrically powered vehicles are used since MoD/MaaS systems potentially require fewer vehicles. The latter can be accompanied by resource, energy, and emission reductions in the vehicle manufacturing phase. However, so-called rebound effects can cause the attractive offer of a new form of mobility to induce demand for its services, thereby limiting positive environmental impact. Furthermore, MoD/MaaS systems require a significant amount of empty runs between passengers’ destinations and the subsequent pick-up locations of additional customers. Such repositioning, which is also necessary for car-sharing, bike-sharing, and e-scooter systems, creates additional traffic, which burdens the urban space. Therefore, careful operational planning of autonomous cab fleet operations (Yi and Smart, 2021) and assessing the environmental impact of autonomous cab services requires a holistic lifecycle-based evaluation approach (Gawron et al., 2019). Finally, autonomous air cabs are currently being developed, e.g., Volocopter (2022). However, whether these will ultimately improve sustainable mobility and livable cities is not foreseeable. The technological requirements, regulatory framework, and the potential lack of participation of all parts of a city’s population create a substantial challenge to implementing such systems. 4.2

Future: Logistics

Recent research has produced promising concepts and findings for collaborative transport to improve the sustainability of logistics in the future (Cleophas et al., 2019). In this context, there are different dimensions of cooperation, and the question arises as to who can and should cooperate with whom. On the one hand, various delivery services can cooperate with each other or customers with suppliers. On the other hand, integrating different services in the joint transport of goods and passengers also offers new opportunities for improving sustainability. When different delivery service providers cooperate, this cooperation can be vertical or horizontal. In vertical cooperation, one of the partners takes over the transport up to a depot, where the next partner takes over some or all of the deliveries. In horizontal cooperation, partners coordinate transport routes and deliveries such that only one provider serves certain areas, reducing mileage and, thus, contributing to more sustainable transportation. Deliver deliveries can be planned and executed more efficiently by concentrating on smaller areas. However, the alliance partners must agree on who will take over which deliveries and how to divide the resulting profit. In addition, such cooperation requires

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partners who are also competitors to exchange detailed information and business activities. The possible cost savings offset the resulting coordination effort - a monopolistic solution as in the days of the Bundespost would be ideal from a planning perspective, but hardly feasible today for reasons of competition policy (Chung et al., 2009). When delivery services and customers cooperate, this can mean that customers take over the final part of the transport on the “last mile.” For example, customers may agree to pick up the delivery at a parcel station. In this case, multiple deliveries must only be delivered to a single point in the city instead of being spread across various addresses. This form of cooperation opens up new planning tasks, such as finding the best location and determining the capacity required per parcel station (Rohmer and Gendron, 2020). In this context, the idea of mobile packing stations is also discussed in recent literature (Schwerdfeger and Boysen, 2020). Another approach is the possibility of setting up delivery stations in front of houses or delivering directly to the trunks of parked cars of the addressees (Reyes et al., 2017). However, such concepts place new demands on urban spatial planning or assume all too naturally the possibility of owning a private car and being able to park it directly in front of one’s home. Integrating transport of freight and passenger mobility becomes possible when, for example, the public transport system is not always fully utilized (Azcuy et al., 2021). Such systems must guarantee mobility for all passengers during peak hours but may result in capacity overhangs between peak hours. Shared transportation of freight and passengers can thus efficiently use the limited capacity of urban space. In addition, parcel stations or mobility hubs can be set up at stops for last-mile transportation. But here, too, several issues complicate the planning process: Strategically, planners must decide whether deliveries and travellers share only the infrastructure, e.g., the streetcar network, or also the vehicles (H¨ orsting and Cleophas, 2022). In addition, technical solutions to the loading infrastructure must be installed at selected loading stations. Furthermore, planning routes and departure times should not result in any deterrent disadvantages for passengers. Related pilot projects in practice, such as the G¨ uterbim in Vienna or CityCargo in Amsterdam, often failed due to the effort involved in planning and coordination and the imperative of companies to maximize profit and customer orientation (Cleophas et al., 2019). Autonomous vehicles offer further opportunities for innovation in urban freight transportation and mobility systems. For freight, drones and robots (Lemardel´e et al., 2021) offer less labour-intensive yet highly flexible systems that may be scaled to customer demand. However, tough questions regarding the regulatory framework are widely unsolved so far. Furthermore, societal conflicts arise when autonomous commercial vehicles enter public space, such as robots driving on sidewalks or drones flying the shortest path over any urban area. Potentially, flying is not a sustainable mode for freight transportation due to the high energy demand per delivered unit (Kirschstein, 2020).

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Two Examples from the City of Kiel

To exemplify future urban transport and mobility solutions, we present two systems currently under development in Kiel, Germany. This section first provides an overview of the efforts to re-introduce a light-rail system before outlining efforts to establish autonomous ferries. Light-rail systems were widespread in many cities around the globe in the first half of the 20th century but were replaced - not just in Kiel - since the 1950 s s (Costa and Fernandes, 2012). This development went hand in hand with the spreading of MIVs throughout almost all parts of urban populations, which made rail systems obsolete from the perspective of city governments and most of society. Today, this development seems regrettable, given the current discussion of reducing air pollution and fossil-fueled cars. In Kiel, the discontinuation of the streetcar system took place in 1985. However, since the early 2000 s,s, such a system has been rediscovered as an environmentally friendly form of transport thanks to its full electrification. Concrete public discussion has emerged about whether or not to reestablish such a system and, if so, what technology and service lines to use in particular (von Beckwermert, 2022). In November 2022, the city government finally decided to create infrastructure for a network consisting of four lines to supplement the mobility ecosystem of the city. Figure 2 shows a design impression integrating the light-rail system into the newly transformed Holsten quay in Kiel downtown. This system also creates opportunities for the combined transportation of passengers and freight. Last but not least, many urban centres were historically built along watercourses or riparian areas, which is why water plays a significant role in urban mobility. Bridges, tunnels, or bypasses can cross waterways by land transport. In addition, ferries can be used for larger bodies of water. For the city of Kiel, which occupies the area around the Kiel Fjord, passenger ferries are the only means of transportation for a direct crossing of the water body. Currently, two ferry lines operate on a regular schedule using older fossil-fueled and modern electric vessels. Implementing autonomous ferries is expected to resolve the lack of crew labour observed for conventional ferries. Additionally, using a larger number of smaller units opens up the potential for more frequent services, probably even MoD. In this direction, the CAPTN Initiative (CAPTN, 2022) seeks to develop autonomous passenger vessels and associated integrated mobility chains. Interdisciplinary subprojects research these options from diverse yet integrated perspectives, e.g., by conceptualizing passenger-friendly novel ship designs (see Fig. 3a and CAPTN Vaiaro (2022)) and by currently constructing an experimental vessel (see Fig. 3b and CAPTN Wavelab (2022)). In these projects, research expertise from various fields such as industrial design, engineering, informatics, business management, social science, and urban planning aims to achieve a holistic view of the development of future mobility options. In this line, flexible service concepts that integrate MoD-ferry services for low-demand ports with regular liner services for highdemand ports are investigated by Aslaksen et al. (2021), see Fig. 4.

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c City of Fig. 2. Design impression for the new streetcar system in Kiel downtown ( Kiel / Rambøll/Dreiseitl).

c Vincent SteinhartFig. 3. (a) Design of the floating platform “CAPTN Vaiaro” ( Besser, Simeon Ortm¨ uller/Muthesius Kunsthochschule Kiel); (b) Experimental vessel c Vincent Steinhart-Besser/CAPTN “MS Wavelab” currently under construction ( Initiative).

Fig. 4. Illustrating example for the combination of regular ferry services (red ferry symbols) and flexible MoD-services (grey ferry symbols) on the Kiel fjord. (Aslaksen et al., 2021)

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Conclusion and Research Perspectives

Urban mobility has undergone tremendous changes in the past, it does so at present, and it will undoubtedly continue to do so in the future. This essay attempted to shed light on several of these developments over time. Which of the various forms of mobility discussed here prevail in the medium and long term and, thus, shape the cityscape of the future, is difficult to predict. For all private-sector offerings, market demand will, in principle, determine success. However, public-sector offerings will undoubtedly be more strongly driven by political decisions and social discourse, e.g., on sustainability. Intensive research efforts currently consider all forms of present and future mobility. Next to questions of technological development, sustainability, public acceptance, service quality, and economic viability are particularly important for the development and operation of future mobility systems. In this respect, logistics management should not be neglected in designing such systems, as it considerably impacts the achievable profitability, emissions, service quality, and transport capacities. However, solely focusing on the perspective of optimizing efficient logistics is likely too narrow, given the complex socio-, economic-, and eco-systems that form contemporary urban areas. Therefore, we finally suggest some approaches for broadening the perspectives of logistics management research: • Holistic comparisons: As city planners primarily have to decide which systems to implement and how research may gain more impact if it supports comparing alternative systems rather than “over-optimizing” particular designs. However, a useful comparison of systems also requires a deep understanding of each system’s operations and capabilities. • Interdisciplinary perspectives: Since successful urban transportation systems have so many facets, research should combine the expertise of logistics management with other relevant areas, such as urban planning, engineering, informatics, social sciences, etc. • Stakeholder involvement: Researchers and planners should foster the inclusion of relevant stakeholders like public transport operators, city governments, citizens, city governments, etc., as these are the promoters of implementing new solutions. This is because urban development is driven by society and local governments. For example, thinking from a community perspective and in terms of city districts (Stadtquartiere) often reveals guiding principles that must be respected and considered when designing transport systems from a management perspective. • Case studies: As cities are quite diverse in their topology, infrastructure, mobility demands, etc., case-study-driven research could contribute to showing the applicability of solutions that account for these particularities. In return, research on general planning approaches may be inspired by realworld accounts. • Multi-criteria decision making: At present, several developments coincide in urban transport and logistics, such as the call for more sustainability, the development of autonomous driving, mobility sharing services, and

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populations demanding shifts in the use of public space. Planning models should explicitly allow for multiple, potentially conflicting objectives, such as social and ecological sustainability, to create a more holistic perspective that accounts for the interdependencies of those trends. • Inspirations from the past: Cities evolve over time: Historical developments have shaped today’s cities and, thus, are the foundation of future developments. The design of future transport systems should consider this by reflecting on what developments were successful in the past and including perspectives on the mid- and long-term developments for urban areas. Finally, at this point, we would note that many developments described here are not necessarily limited to urban spaces. In rural spaces, drones and autonomous driving offer new opportunities to cover large distances and difficult terrain to deliver medicines or other supplies quickly in cases of emergency (Ghelichi et al., 2021). Of course, the concepts described for urban transport frequently rely on established infrastructure and are suitable only for short distances, e.g., e-scooters and delivery robots require smooth runways and cover distances of at most a few kilometres, which is not suitable for rural areas. Additionally, the longer distances and lower population densities in rural areas require significant lead times in MoD- and MaaS-systems, limiting ad-hoc service capabilities (Johnsen and Meisel, 2022). Eventually, depending on the outcome of the noted conflict of agglomeration versus urban sprawl, suburban areas may either grow further or split into more sharply demarcated urban versus rural areas. We hope this essay contributes a new perspective for future logistics research. Such research is called for developing capable transport solutions in liveable cities.

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Heterogeneous Rail Supply Chain Strategies for International Rail Transport Jing Shan1(B) and J¨ orn Sch¨ onberger2 1

2

Institute of Railway Systems and Public Transport, TU Dresden, Gerhart-Potthoff-Bau, 108 Hettnerstr. 1, Dresden, Germany [email protected] Chair of Transport Services and Logistics, TU Dresden, W¨ urzburger Straße 35, 01187 Dresden, Germany [email protected]

Abstract. The increasing diversity of goods structures in the global supply chain has led to a need for varying inventory policies and different rail services. Understanding the heterogeneity of services in international rail transport is essential to meet these needs effectively. Existing research has focused on market segmentation in other transportation sectors, such as passenger transport, airfreight, and maritime transport. Previous research on the global supply chain and rail system has primarily been conducted isolated, whereas this paper focuses on the intersection of the two systems. This area of research is relatively novel and requires deep exploration. Using “Lean” and “Agile” principles, this paper proposes efficient, continuous replenishment, and responsive rail supply chain strategies that support market segmentation and service differentiation. These strategies ultimately contribute to integrating rail transport into the global supply chain by improving and diversifying international rail services. Additionally, managers of rail and logistics companies can benefit from this research by better understanding the value creation process, the specific characteristics of international rail transport, and the requirements of global supply chains.

1

Introduction

Globalization has led to a significant increase in the need for border crossing transportation due to the growing distances between suppliers, producers, and final customers. This has resulted in various requirements for international rail services. The Eurasian rail transport is such a successful example, evolving into an intercontinental rail network that spans multiple rail lines, as shown by the red lines in Fig. 1. Such as the trans-Kazakhstan, Mongolian, and Trans-Siberian lines, as well as routes through Belarus and Poland. It faces several challenges due to its involvement in different rail systems (China, CIS, and Europe) and complex rail operations at the border crossing terminals. The difference in railway gauges between CIS countries (1,520 mm), China (1,435 mm), and Europe c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 131–145, 2023. https://doi.org/10.1007/978-3-031-38145-4_8

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(mostly 1,435 mm) requires the transshipment of containers at border crossing terminals when entering different rail systems.

Fig. 1. The current layout of the Eurasian transport Network (Own Illustration)

In recent years, many companies have turned to Eurasian rail transport as an alternative to sea and air transportation between China and Europe, considering it a backup plan [25]. However, due to the increasing diversity in the goods structure of the global supply chain, some high-value products require fast and reliable services, while others prioritize lower prices. The value density of goods in each container transported can vary greatly, with lower value density associated with higher transportation, storage, and handling costs and greater price sensitivity. More service differentiation among Eurasian rail services is needed to meet the varying transport service needs of the global supply chain. Long-distance international rail transport is well-suited for service differentiation, given the longer order placement period. However, the current Eurasian rail services are primarily classified based on the loaded and empty containers for the main transit, and discounts are offered based on the number of containers [29]. Service differentiation in international rail transport requires more research. LSPs (Logistic service providers) and 4PL are more inclined to offer customized operations and differentiated services to their clients than the carriers who are asset owners, Fig. 2 shows the general relation between the type of services and the degree of customization [17]. However, mass customization in logistics influences logistic service providers’ decision-making [34]. And research on customized logistics services has increased in recent years [22]. Market segmentation research in the transportation sector has primarily focused on passenger transport, airfreight, and maritime transport [31].

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Fig. 2. Degree of customization and scope of services of participants in the railway supply chain (adapted from [17])

This paper aims to partially bridge the gap between the service differentiation of international railway transport and the global supply chain market segmentation by developing multiple rail supply chain strategies. These strategies aim to transform heterogeneous customer demands into different rail services. The underlying concept is that, while each customer’s needs are unique, the goal of achieving economies of scale in rail transport makes it impractical to plan for each order. This paper categorizes supply chain needs with similar demand characteristics into several groups to address this. Each group requires a unique rail supply chain strategy, such as efficient, continuous replenishment and responsive rail supply chain strategies. Rail supply chain strategies typically define different service classes for market segmentation, including Standard, Express, and Ad hoc services. Within each service class, there can be further classification based on different service levels and various service quality attributes. This paper is structured as follows, Sect. 2 reports existing research on service differentiation. Section 3 develops efficient, continuous replenishment and responsive rail supply chain for strategies. Section 4 uses those rail supply chain strategies to differentiate international rail services. Section 5 discusses the developed rail supply chain strategies, rail service differentiation and potential applications.

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Existing Research on Rail Service Differentiation

This section analyzes the market segmentation requirements of the rail industry and investigates whether rail service differentiation is present in the planning process. Furthermore, the section aims to identify the advantages and disadvantages of service differentiation. In addition, the research gap will be highlighted. Different supply chain demands have varying inventory policies and rail service requirements. For example, some manufacturers may require just-in-time production with minimal inventory, while others may purchase and stockpile components in bulk without regard for delivery speed or timing. However, the heterogeneity of rail service is overlooked in Eurasian rail transport. We analyzed 841 containers transported from China to Europe between Dec. 2020 and Nov. 2021. We found significant variability in the value density of each container on a train, as demonstrated in Fig. 3. Lower value density was associated with higher transportation, storage, and handling costs and increased price sensitivity. Service differentiation in international rail transport requires more research and affects the rail transport’s logistics capability. The logistic capability of railway services is the competence to create market-oriented mass-production logistics solutions and individual customer-oriented logistics solutions [27]. Rail planning needs to consider rail service heterogeneity to cater to the varying preferences of shippers for transport service characteristics. Current service network design cases and models need to be revised as they only consider regular demand and do not fully consider the diverse transport requirements of the global supply chain. Several service quality criteria are not considered simultaneously [1]. While some transport planning models consider shipper heterogeneity regarding transport time and reliability preferences, they often only consider transport services in isolation. The value of time (VOT) is critical in determining the priority of time-sensitive products [2]. Previous studies have considered VOT and VOR (Value of reliability), demonstrating the potential for improving network service levels in China [12]. However, effective integration of rail planning with the transport demands of the global supply chain is challenging due to the need for rail service differentiation. As market segmentation is always associated with revenue management, railway operators can develop pricing and capacity management policies to differentiate services for their high-priority customers and increase profits. The importance of rail demand analysis and the potential benefits of customer segmentation in optimizing the allocation of railway resources have been highlighted [32]. This approach can also help smooth demand, reduce network congestion during peak periods, and efficiently use rail resources [5,28]. However, revenue management problems in the railway industry have received less attention than in the airline industry [3]. Nowadays, the main challenge is often overbooking, which could hurt service quality. Railway operators may reject high-value customers due to low-value demands occupying total train capacity. In today’s increasingly competitive environment, revenue management is crucial for international railway planning, primarily due to this transport mode’s high capital costs and low margins. The recent drop in freight rates between Asia and Northern Europe

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Fig. 3. Value Density analysis of 841 containers from China to Europe between Dec.2020 and Nov.2021 from Company B

since December 2022 will significantly impact Eurasian rail freight demand [15], making revenue management more critical than ever. It is worth noting that a high level of customization can result in a conflict between customization and economy of scale, leading to a massive cost of production. Therefore, it is crucial to define a rational customization level [22]. In addition, heterogeneity can impact rail capacity, but the effects depend on the type of heterogeneity; for example, different types of trains have varying maximum speeds, accelerations, and braking distances, which can substantially affect rail line capacity [16]. This section stresses the need for further research on various international rail services to improve logistics capabilities and revenue management. The study highlights the challenges of integrating service differentiation into rail planning due to a lack of research on market segmentation and service differentiation. The research on heterogeneous international rail services, which involves multiple border crossings and varying rail gauges, is a new area of research. The following sections will classify transportation demands based on various rail supply chain strategies.

3

Development of Railway Supply Chain Strategies

This section attempts to look at the global supply chain and the international rail system as a whole. It develops to formulate rail supply chain strategies that align with widely accepted “Lean” and “Agile” principles in supply chain management. These strategies are designed to facilitate market segmentation and differentiation of rail services.

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Fig. 4. Rail supply chain strategies based on “Lean” and “Agile” principles [8]

Efficiently meeting the global supply chain’s diverse transport demands requires integrating both demand and supply characteristics from the supply chain and international rail system. It is essential to view the international rail system and the global supply chain as an integrated network involving shippers, logistics service providers, and railroads in multiple countries to accomplish this. This requires integrated planning, coordination, and control from origin to destination terminals. Rail supply chain strategies are essential to better control this network, with different customer demands requiring different strategies to compete in the market. The right rail supply chain (RSC) strategy is determined by demand and supply characteristics, specifying the responsibilities of shippers and railway operators and the level of service to be provided. Rail supply chain (RSC) strategies specify rules of the market and rail service segmentation, as shown in Fig. 5. Market segmentation involves dividing the market into homogeneous groups using various variables. By linking services to these customer and market segments, carriers can differentiate themselves, and better position their service in the market [31]. To balance each customer’s unique needs with the economy of scale in rail transport, rail supply chain strategies are designed based on “Lean” and “Agile” principles, which classify strategies based on lead time requirements and information availability of booking from shippers. Three main RSC strategies are used: Efficient, Continuous Replenishment, and Responsive, as shown in Fig. 4, which fulfill the different transport needs of the supply chain. Demand characteristics are related to the availability of information. With the advancements in information technology, obtaining customer demand data at the planning stage is possible, enabling a priori segmentation. Demand information is known beforehand, such as booking details including origin, destination, average physical volume and weight, departure time, and delivery due time [9,11,13,23]. On the other hand, post-hoc segmentation is used when demand is

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uncertain, and empirical data is necessary to establish a segment [19]. The Efficient and Continuous Replenishment of RSC strategies require advanced booking and information availability, as depicted in Fig. 4. Rail operators tailor their service levels to meet the diversified demands of the supply chain based on the specific RSC strategy being used, as shown in Fig. 5. By grouping similar supply chain demands and applying the appropriate RSC strategy, rail operators can meet the needs of their customers while optimizing efficiency.

Fig. 5. Market segmentation and service differentiation

3.1

Efficient RSC

In an efficient rail supply chain strategy, make-to-stock is frequently used, with long lead times and predictable demand [7]. The Make-to-Stock supply chain planning method is suitable for Supply chain demands that are highly certain. Railways pursue the maximum operational throughout (Booking spaces measured with TEU-twenty-foot equivalent unit) and utilization of railway resources. As soon as railway services are produced, they will be consumed, the transported goods could be stored, and there will be no loss during the transformation from outputs to consumptions [20]. Traditional North American railroad operating policies were based on long-term contracts for transporting high volumes of bulk commodities , with cost per ton/mile (or km) being the primary performance measure and delivery performance receiving little attention [10]. 3.2

Continuous Replenishment RSC

Advanced supply systems have emerged in today’s fast-paced and competitive markets, focusing on just-in-time and just-in-sequence deliveries using intermodal freight transport solutions. These time-sensitive products are often delivered as soon as they are manufactured, with short replenishment lead times

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and low inventory levels. Integrating express international rail services into the replenishment process is crucial to maximizing inventory velocity in the supply chain. This can be achieved by actively involving the supply chain in the rail service planning process and considering production and transportation planning activities simultaneously, in collaboration with rail forwarders, logistics service providers, and shippers [27], rather than simply receiving offerings from transport providers. This pull production principle can ensure that supply chain requirements are integrated into the rail planning. Unlike traditional approaches, which first focus on production planning and then on transportation decisions, this approach neglects the coordination between the production schedule and the distribution plan. The recent scarcity of containers in the global supply chain in 2021 has emphasized the need for shippers and rail transport operators to understand the specific characteristics and requirements of value creation to adapt to this new reality. The integration of production and distribution is a crucial issue in supply chain management, which has been extensively discussed in academia and industry [18]. To achieve this, it is necessary to determine the amount and timing of goods at the departure railway terminal during the planning and ensure timely delivery to customers through continuous replenishment. Costs associated with early gate-in of containers and delivery penalties can be incurred if containers arrive at the destination terminal before or after the scheduled due date. Additionally, delay penalties cost is caused due to potential loss of sales, truck waiting costs, and possible production shutdowns due to material shortages. 3.3

Responsive RSC

The dynamic nature of the global supply chain presents additional challenges to international railway transport. Short-term planning is needed to accommodate customers who require orders or adjustments to be made in a short period before departure, as opposed to the conventional practice of international rail freight with a long planning time. It differs from continuous replenishment and efficient railway supply chain strategies, which are fixed and planned over a relatively long period. On the other hand, the Responsive RSC strategy requires ad hoc transportation bookings to be completed quickly, resulting in additional expenses and resources reserve. Railways face difficulties in accepting ad hoc demands, but it presents a potential market for international rail transport, requiring a reserve of additional capacity to adapt to uncertain demand. All the partners of the railway system must have extra capacity to respond to such volatility. The risk of poor efficiency arises when the additional capacity is only partially utilized. A responsive rail supply chain strategy plans the timetable and reserves the railway resources and personnel depending on the distribution pattern or average booking rate based on historical information. A mismatch of demand forecast and railway resources may result in service deficiency or underutilization of railway resources [33]. Ad hoc demands may receive services with medium lead times less reliable than high-priority express services.

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The following section will discuss how to differentiate the international railway service and its associated service quality under the various railway supply chain strategies.

4

International Rail Service Differentiation Table 1. Eurasian rail transport customers’ most desired service attributes [24] Quality Factors

Responses Percent

Speed Transport time reliability Departure frequency Environmentally friendliness Safety Price

66 87 41 16 35 66

49% 65% 31% 12% 26% 49%

Railway operators must define different service classes to meet the varied requirements of shippers. However, achieving efficiency and responsiveness in rail supply chain strategy in the same rail service can be challenging. Different rail services, including Standard, Express, and Ad hoc services, are required under various rail supply chain strategies to tackle this issue. Carriers can differentiate themselves by linking services to customer and market segments, better positioning their services in the market [31]. Service differentiation is achieved through flexible service options, and a careful selection of variables is necessary to unify market segmentation and service differentiation. Acceptance of these variables by the supply chain and rail system is essential. The aim of service differentiation via market segmentation is to provide customers with appropriate service based on their sensitivity to rail service attributes[5]. Any attribute that customers value and is essential to shippers can be used to differentiate service levels [4]. Transport time, reliability, frequency, flexibility, sustainability, traceability, priority, and security influence customer perceptions and mode selection [6,14]. Performance indicators such as speed, reliability, and price influence the choice of Eurasian rail transport mode, as evidenced by an online questionnaire [24], as shown in Table 1. Operational performance parameters, such as reliability, are significant in determining rail service performance [27]. The availability and quality of information are critical to providing effective planning and control of service provision processes. Limited information poses a challenge when making decisions in transportation planning [21], leading to suboptimal decisions and higher costs for the entire transportation network [26]. We selected key performance parameters such as reliability, responsiveness, and predictability (the availability of information) to determine the service level, as depicted in Fig. 6.

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Fig. 6. Potential service classes of international railway transport

Railway transport is considered to be heterogeneous when trains have significant running time differences on the same track sections. In contrast, it is considered homogeneous if all trains have similar characteristics [30], especially the same transit time and loading, unloading, and transshipment time at the border crossing point. Service differentiation is possible by developing loading and unloading plans at terminals, different transit times, and train connection standards for the border crossing. This can be done by implementing priority rules, as shown in Fig. 7. Service differentiation of international rail transport through varying transit times and border crossing times is a feasible option for congested rail networks. Express delivery is considered a higher quality service with guaranteed delivery time. Express service is for urgent cargo and receives priority for locomotives, handling, personnel, and slot time of tracks and terminals. Standard service is for non-urgent shipments and agrees to some days longer than express delivery at border crossing points. The border crossing terminal and the transit time between two border crossing terminals are the main bottlenecks in Eurasian rail traffic. Based on the priority rules in Fig. 7, a standard service could generally take a few days longer than an express service at the border crossing terminal and during transit.

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Fig. 7. Service differentiation for broad gauge transit and border crossing terminals

• The efficient use of railway resources is a priority in a standard rail service, which focuses on mass production rather than speed. To ensure this efficiency, the customer must deliver the shipment at the destination terminal at the agreed-upon time, with a cut-off time typically several days before the train’s departure. Failure to do so results in loss of reserved space and non-refundable fees. • In the context of the continuous replenishment RSC, express service requires high speed, reliability, and timely information exchange, making it suitable for urgent cargo delivery. In this service, priority is given to shipment from the origin terminal to the destination terminal, with a short cut-off time before the train’s departure. To ensure seamless transportation, it is essential to maintain timely connectivity at border crossing points. Railway operators often take measures such as reserving rail fleets and incorporating capacity and time slack into the scheduling process to mitigate potential delays. These measures help ensure that cargo is delivered promptly, meeting the demands of the global supply chain. • Ad hoc services are provided when sufficient capacity is available, such as East Bound trains from Europe to China. To avoid conflicting with high-priority express services, ad hoc demands may receive medium lead time services that are less reliable than express services.

5

Discussion

Effective coordination and information sharing among shippers, railway operators, and terminals are essential for a successful continuous replenishment RSC strategy. Sharing vital information such as volume, release time, lead time, and inventory status can optimize rail planning and delivery processes. Advances in information technology enable shippers to be more involved in the railway’s planning process, with ERP and TMS integration providing automation and enhancing customer experience. This is especially crucial for express international rail services that involve complex administrative procedures before the release of goods. A digital platform connecting shippers, forwarders, and railways can

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improve predictability. Permissioned blockchain technology can enhance accuracy and speed in planning, resulting in better schedule adherence and resource utilization. A responsive rail supply chain strategy is crucial for effectively addressing unexpected demand in the rail industry. In implementing a responsive rail supply chain strategy, it is essential to shorten railway planning time and reserve capacity in advance, which allows swift transportation execution upon receiving an order without extensive paperwork and planning. Rail planning must incorporate advanced analysis and forecasting tools to anticipate and minimize deviations between forecasted and actual demand as much as possible to achieve a responsive rail supply chain strategy. It’s worth noting that relying solely on past demand data can be inadequate due to the global supply chain’s complexity and unforeseen consequences. The ad hoc service should only be offered when there is abundant railway capacity to ensure optimal rail performance and minimal impact on other services. Rail companies can adopt either a single rail supply chain strategy or a combination of different strategies. Even under the same rail supply chain strategy, different service levels can be achieved by adjusting reliability. The challenge lies in meeting multiple service requirements of shippers while efficiently using railway resources, where the pursuit of service quality can negatively impact rail efficiency, and vice versa. By combining different rail supply chain strategies, revenue management can help balance service quality and efficiency. However, before implementing a responsive rail supply chain strategy, it is essential to focus on achieving efficient and continuous replenishment rail supply chain strategies.

6

Summary

This paper considers the global supply chain and the international rail system as a whole, which is a relatively new research area. To support market segmentation of supply chain demands and rail service differentiation, efficient, continuous replenishment, and responsive RSC strategies have been developed based on “Lean” and “Agile” principles. Service classes are derived, including Standard, Express, and Ad hoc services. Service levels are specified according to demand and rail operational characteristics, mainly predictability (availability of information of demand), responsiveness and reliability (rail operational characteristics). To differentiate the responsiveness of international rail transport, priority rules for main transit and border crossing could be implemented. This paper lays the foundation for future research into the international rail supply chain, although it does not cover all global supply chain requirements. More research is needed to look deeper into the various service classifications of international rail transportation and to validate the various rail supply chain strategies using planning models.

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Collaboration Benefits in Port Hinterland Transportation Nicolas Rückert1(B)

, Kathrin Fischer1 , Pauline Reinecke2 and Thomas Wrona2

,

1 Institute for Operations Research and Information Systems, Hamburg University of

Technology, Am Schwarzenberg-Campus 4, 21073 Hamburg, Germany [email protected] 2 Institute for Strategic and International Management, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073 Hamburg, Germany

Abstract. Port hinterland transportation with trucks is an important part of the maritime supply chain as a significant part of supply chain costs are generated here, e.g. due to empty container transportation. Horizontal collaboration among carriers offers potential for cost reduction, but needs to be set up in a way which is fair and advantageous in the long-run for all carriers involved. In this work, a new model is developed which takes these and other realistic requirements, e.g. the use of different container types and of empty container depots, into account. The results allow to quantify the benefits of collaboration for different collaborating group sizes and under different fairness mechanisms. Keywords: Collaboration · Cooperation · Hinterland Logistics · Optimization · Fairness

1 Introduction Container shipping is at the core of worldwide transportation and builds the backbone of international trade. At the same time, growing ship sizes and increasing numbers of containers mean challenges for ports as well as for hinterland transportation. The latter is responsible for 40 to 80% of the costs in the maritime supply chain (Notteboom and Rodrigue, 2005; Tran et al., 2017) and hence of special interest with respect to optimization. While different means of transport – train, inland vessel, truck - can be used, transportation by truck is mostly preferred in hinterland transportation due to its flexibility. However, container transportation in the port hinterland has to face special challenges. While container vessel sizes can be (and are) increased, the size of trucks is fixed. A larger number of containers therefore can only be transported by a larger number of trucks and/or on a larger number of trips, leading to emission increase and strains to the road infrastructure (Notteboom und Rodrigue 2009, S. 21). An additional important aspect is the empty container trip problem. In the port hinterland, many trips occur where a container is delivered and then the truck has to return © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 146–161, 2023. https://doi.org/10.1007/978-3-031-38145-4_9

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empty to its depot. Moreover, imbalances in trade contribute to the problem (Song and Dong, 2015). When imbalances (e.g., between the port hinterland and seaport terminals) occur, the supply and demand of empty containers diverge. Trade imbalances thus require compensatory trips to relocate empty containers (Theofanis and Boile, 2009). They are further exacerbated by a lack of information sharing between actors in the hinterland (Islam et al., 2013; Song and Carter, 2009). The economic consequences of empty trips in the hinterland are considerable as 40% of all containers are empty – twice as many as in maritime shipping (Konings, 2005). Inefficiencies in this regard therefore represent “one of the most complex problems concerning global freight distribution” (Notteboom and Rodrigue, 2008, p.168). The ‘empty trips problem’ (Islam et al., 2021) prevents carriers from maximizing their transport capacity utilization, leads to additional costs, and causes an increase in emissions (Islam et al., 2013; Song and Dong, 2015). In Germany, for example, the utilization factor of container transportation was only 39.5% in 2020, mostly due to empty trips (German Federal Transport Authority, 2020). One way the empty trips problem can be addressed is by collaboration, which can be designed as vertical (e.g., between shippers and carriers) and horizontal (e.g., between carriers) (e.g., Caballini et al., 2016; Pan et al., 2019). Horizontal collaboration in logistics can be defined as activities which bundle the transportation of companies that operate at the same level of the supply chain and have similar or complementary transportation needs (Vanovermeire et al., 2014, p. 340). Although the benefits of collaboration in the hinterland have been discussed (e.g., Aloui et al., 2021; Guarjardo and Rönnquist, 2016) and evaluated (e.g., Gansterer and Hartl, 2018, 2020; Pan et al., 2019), horizontal collaboration of carriers in the hinterland has received less attention than vertical collaboration (Sanchez Rodriguez et al., 2015; Islam and Olsen, 2014). However, sharing trucks or orders among carriers in the hinterland provides opportunities for reducing empty-truck trips, creating more efficient trips and thus improving logistical efficiency (Islam et al., 2013, 2021; Krajewska et al., 2008). Nevertheless, in practical settings it turns out that carriers are very reluctant to participate in collaboration and to share information and orders with their competitors, or that they are only prepared to do this with a selected group of companies. A grounded theory study with freight forwarders and carriers in the port hinterland (Reinecke et al., 2023) revealed that trust between competitors is a major prerequisite for collaboration, but is often lacking due to high industry rivalry (because of low margins and low entry barriers) and due to the fear of disclosure of sensitive information. However, there is a group of proactive companies which, despite the difficult market situation, emphasize the importance of collaboration and already cooperate on a rather informal level by, e.g., communicating via email and occasionally passing on customer orders to trustworthy competitors. Therefore, this paper is aimed at the development of a new model for the operative optimization of container transportation in the port hinterland which takes important practical aspects such as the use of empty container depots and requirements as formulated by carriers – in particular the fact that they are only willing to collaborate with selected carriers – into account. The major objective which distinguishes the approach

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from others is to derive a model leading to collaborative solutions which are fair and stable in the long run, i.e. do not lead to a marginalization of some of the carriers. The remainder of this paper is organized as follows: In Sect. 2, relevant literature on horizontal collaboration, especially in (hinterland) logistics, is discussed. In Sect. 3, aspects of the new model are presented. In Sect. 4, some results are presented and discussed. Sect. 5 gives a conclusion and an outlook.

2 Literature Review: Optimization of Collaboration in Hinterland Logistics The increasing importance of horizontal collaborations in all logistics areas is reflected in the mounting scientific interest in the topic (e.g., Cruijssen et al., 2007; SerranoHernández et al., 2017) and the exploration of themes such as motives, opportunities and challenges. For instance, Sanchez Rodriguez et al. (2015) find in their empirical study of collaboration in retail supply chains that cost reduction is the most important motive for collaboration and trust is an important prerequisite. Similarly, based on a literature review, Pomponi et al. (2015) develop a framework for collaboration, emphasizing the role of trust and communication. Likewise, Serrano-Hernández et al. (2017) analyze the key benefits and challenges associated with horizontal collaboration, also highlighting the importance of mutual trust between actors, which is often lacking in reality. Finally, in their review of the ‘solution’ (e.g., carrier collaboration or transport marketplace) and implementation (e.g., collaborative network design or communication technology) used in horizontal collaborative transport, Pan et al. (2019) emphasize the importance of (real-time) communication processes. While the aforementioned studies concentrate on horizontal collaboration, they neglect the specifics of container transportation in maritime hinterland. For carriers, the exchange of orders and the planning and use of joint routes usually is the most important issue to reduce travel distances, empty vehicle movements and the number of trucks by route optimization (e.g. Gansterer and Hartl 2018, Himstedt and Meisel 2021). The situation in maritime hinterland transport is somewhat similar with respect to some of the goals and challenges, but the planning problems that companies have to solve are different due to the transportation of complete containers, which limits the options of combining trips, the peculiarities of the import and export processes via a port, which require more empty trips (Islam and Olsen, 2014), and the necessity of empty container depots. There are only a few Operations Research (OR) oriented publications which explicitly consider container transportation by truck in the maritime hinterland. In the following, the results of a systematic literature review of the field are briefly presented. In total, nine relevant publications were found. Four of the publications consider the sharing of capacities by exchanging, e.g., empty containers (Kopfer et al. 2010, Luo et al. 2021, Sterzik et al. 2015, Widjanarka et al. 2018.), while the other five publications consider the exchange and/or combination of customer orders; these are Caballini et al. (2016 and 2017), Calore et al. (2017), Irannezhad et al. (2020) and Uddin and Huynh (2020).

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It was found that none of the publications explicitly takes empty container depots into account; often, they also do not differentiate between empty trips (truck travelling without container) and empty container transportation. In terms of mechanisms for establishing stable collaboration, these are only implemented in five of the above-mentioned publications. Three of them use compensation payments (Caballini et al. 2016, Calore et al. 2017, Luo et al. 2021). Some employ mechanisms which enforce that each participating carrier has to improve its profit or reduce its costs by the collaboration, or there are penalties in case of a profit reduction (Caballini et al. 2016, Caballini et al. 2017, Calore et al. 2017, Uddin und Huynh 2020). Additionally, the even distribution of the respective improvements among all carriers in order to achieve “fairness” is considered by Uddin und Huynh (2020), concentrating on costs. None of the publications, however, considers other aspects than profit or costs. In particular, the number of orders that each carrier serves is not studied although this can be considered highly relevant for establishing stable long-term collaboration. Obviously, in practice no carrier would like to be left with only a few or even no orders to serve, as just receiving compensation payments can be expected soon to lead to its total elimination from the market. Therefore, this work strives for a balanced distribution of profit gains and served customer orders among the carriers. In their numerical studies, all authors find that total profit can be increased (or costs can be decreased) by collaboration. But none of the approaches takes into account that in reality carriers are reluctant to collaborate with too many others and that fair and long-term collaboration needs to be supported by appropriate mechanisms. This work is aimed at closing these gaps.

3 Planning Situation and Model Development The mathematical model developed here is based on the model suggested by Calore et al. (2017) which was extensively improved and expanded. Their model is particularly suitable because it employs compensation payments as well as a fairness mechanism. In addition, in this work modifications are made to achieve long-term stability of the collaboration. The planning situation is such that two or more hinterland carriers (of a set of carriers R) have to carry out different container transports for a known number of customers (N ). It is to be decided which of the carriers should fulfill which of the transportation orders, i.e. whether (and which) exchange or combination of orders is advantageous for the carriers, and in which sequence and combination the orders are to be carried out. A set of customer orders, each containing a container transport request (from an origin on (n ∈ N ) to a destination dn ) for one single container, are given. In the case of import customer orders (I ⊂ N ), a fully loaded container is to be transported from a seaport terminal to a location in the hinterland and then (after unloading) transported from this location to an empty container depot. In the case of export customer orders (E ⊂ N ), a fully loaded container is to be transported from a location in the hinterland to a seaport terminal. To do this, the carrier must first collect an empty container from an empty container depot. A set of empty container depots (KCD) is given and each customer order is assigned to one of those depots cdn in advance.

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A container transport must be carried out in its entirety by a single truck, resulting in service times for the truck during loading and unloading of the container (at origin: ston , at destination: stdn , at empty container depot: stl i , i ∈ KCD). Each trip with one or two combined customer orders has to be performed at one day t ∈ {1, . . . , T }, with each order having a “desired” day and a hard day-related time window. More than two container transports, however, cannot be combined. Each customer order generates a revenue RC n when executed. Each container belongs to one of three shipping alliances (in reality 2M, THE or Ocean Alliance), given by Alliancen , as it is assumed that the shipping lines make their containers available only to other members of their alliance, and to one of four container types Typen . The container types taken into account are 20-foot containers (TEU 20’), 40-foot containers (40’), 40-foot highcube (40HC) and 45-foot highcube containers (45HC). The use of heterogeneous containers (Funke and Kopfer, 2016), the aspect of the container owner and the container type (Lei and Church 2011) have not been considered in OR for collaborative transports in the port hinterland before, increasing the practical relevance of the model developed in this paper. Figure 1 shows the standard case with single trips without collaboration and the advantages of collaboration for hinterland container logistics with trucks. Truck carrier 1 with truck depot 1 (light shades) owns one import customer order (blue) and truck carrier 2 with truck depot 2 (dark shades) owns one export customer order (green). Both truck carriers can execute their customer orders as single trips (left). Note that each order/trip involves an empty container depot, either before (export) or after (import) the rest of the trip. When carriers collaborate, they can pass on their customer orders to another carrier, i.e. the “order owner” can hand over the customer order to another carrier, referred to as the “order executor”. Through this collaboration, combination trips (also called round trips) are rendered possible (center figure), which reduces the number and length of empty trips and empty container trips. Import customer orders can be combined with export customer orders, resulting in the set P := {(n, m) : n ∈ N ∧ m ∈ N }, which contains the sets PIE := {(n, m) : n ∈ I ∧ m ∈ E} and PEI := {(m, n) : m ∈ E ∧ n ∈ I }. In this particular example, carrier 1 (current order executer) can execute the customer order owned by carrier 2 (former order owner). Figure 1 (middle) visualizes the sequence of locations which carrier 1’s truck has to visit. Note that one or two empty container depots might be involved between the import and the export order; the latter case is shown in the figure. Obviously, a combination trip could also start with an export customer order and end with an import customer order. A special case is the combination trip with street turn, which is only possible for the combination of an import order with a subsequent export order with the exact same container (right figure). To model this, the set PIE is defined as: PIE := {(n, m) : n ∈ I ∧ m ∈ E ∧ Alliancen = Alliancem ∧ Typen = Typem } ⊂ PIE). Additional costs occur for the effort of the street turn (Cp), as usually a safety check is required before a new transport of the same container can take place. On the other hand, a street turn saves a trip to and a stop at an empty container depot and can therefore still be worthwhile. 



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Fig. 1. Comparison of different trip types without and with collaboration.

Further aspects describing the customer orders are time slots (hard time windows) at the seaport terminal and in the hinterland location (these must be strictly adhered to) and soft time slots for the destinations, which generate costs when arriving too early (Cen ) or being too late (Cl n ). It is assumed that a slot has already been reserved at the container terminal for export customer orders and that an estimated time of arrival has already been communicated for import customer orders; if these soft time slots are violated, penalties are caused. With regard to the carriers, it is known which customer orders each of them owns (parameter znr ∈ {0, 1}, equals 1 and CarOwnn = r if customer order n ∈ N is originally owned by carrier r ∈ R), how many trucks a carrier can use per day (MaxTruck tr ) and how long a truck may drive per day. The number of customer orders which are to be carried out by carrier r on day t without collaboration and combination is stored in a parameter (katr ). The profit per day without collaboration is determined in advance for each carrier and stored in parameter pP tr . Furthermore, the costs (in e) for a transport per km distance Ckmr (fuel, tolls) and per hour Chr (wage) are given, and it is known where the company truck depot is located (ld r ). Further parameters are the length of the time horizon T, the connections between all relevant locations (set K), and the respective travel time (in h, shij , i, j ∈ K) and distance (in km, skmij , i, j ∈ K). To design a model which fits reality and to support fair and long-term collaboration, several approaches are used: (1) As in practice not all carriers are ready to cooperate with all others, the existence of so-called trust groups is assumed, i.e. groups of trust-worthy carriers within which an exchange of orders can take place, while across trust groups this cannot happen.

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The respective trust groups are defined beforehand, i.e. they do not result from the optimization, but are an input to the model (CarTGr ). However, variation of the size of these groups allows to study the effects of trust group size. (2) An order executor must pay a compensation payment Ccn to the order owner when it takes over the respective order. This compensation payment differs depending on the customer order (based on the importance of the order). (3) Fairness mechanisms are implemented to achieve a balanced distribution of the collaboration benefits for all participating carriers. In the model, the following decision variables are used: t ∈ {0, 1} is 1, if customer order n ∈ N is performed on day t ∈ {1, . . . , T } by xnr carrier r ∈ R as a single trip, 0 else. t ∈ {0, 1} is 1, if the customer orders n, m ∈ N are performed consecutively in a ynmr combination trip, 0 else. ytnmr ∈ {0, 1} is 1, if the customer orders (n, m) ∈ PIE are performed consecutively in a combination trip with street turn, 0 else. Moreover, there are seven continuous decision variable groups with time reference. Of these, three decision variable groups refer to single trips (e.g. fnt for the arrival time at the destination of the customer order) and four decision variable groups refer to t for the arrival time at the destination of the second customer combination trips (e.g. gnm order m). The objective function (1) consists of the total daily profit of all carriers, i.e. the sum over the daily profits DayProfitrt . These daily profits result from the customer order contributions Irt , i.e. the revenue from customer orders minus the transportation cost, from which Drt , the costs for early or late arrivals, and Wk tr , the waiting costs, have to be subtracted. Furthermore, there are compensation payments received, ComP tr , and compensation payments paid, ComM tr (2).   Maximize DayProfitrt (1) 



r∈R t∈{1,...,T }

DayProfitrt = Irt − Drt − Wkrt + ComPrt − ComMrt ∀r ∈ R ∧ t ∈ {1, . . . , T }

(2)

Further constraint groups exist for the definition of the objective function terms, for the calculation of the travel, arrival and waiting times, for the consideration of customer

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orders, carriers and trust groups as well as for the consideration of fairness.

(3) Constraints (3) are required to calculate the profit per day and carrier. They contain the profit contributions for the different types of trips, i.e. import (n ∈ I ), export (m ∈ E), import-export combinations ((n, m) ∈ PIE), export-import combinations ((m, n) ∈ PEI ) and import-export combinations with street-turn ((n, m) ∈ PIE ). Compared to Calore et al. (2017), this restriction was extensively expanded to increase the model’s practical relevance. For example, both costs per kilometer and hourly costs are taken into account here, full and empty container transport and empty transport (including consideration of empty container terminals) are considered explicitly, and the case of combination trips with street-turn was added as well. Restriction group (4) defines the waiting costs which are not considered in the model of Calore et al. (2017). 

t · Chr ∀r ∈ R ∧ t ∈ {1, . . . , T } Wkrt = (n,m)∈P wnmr

(4)

t r r ComPrt =q∈R q=r (n∈N xnt q · Ccn · znr + (n,m)∈P ynm q · (Ccn · zn + Ccm · zm )

+

yˆ t (n,m)∈PIE nm q 

r · (Ccn · znr + Ccm · zm ))∀r ∈ R ∧ t ∈ {1, . . . , T } q

q

(5)

q

t ComMrt =q∈R q=r (n∈N xnt r · Ccn · zn + (n,m)∈P ynm r · (Ccn · zn + Ccm · zm )

+

yˆ t (n,m)∈PIE nm r 

q

q

· (Ccn · zn + Ccm · zm ))∀r ∈ R ∧ t ∈ {1, . . . , T }

(6)

The carrier who executes a customer order receives the respective revenue. In the case of collaboration, the original order owner receives a compensation payment; these are

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determined by the constraints (5) and (6). Furthermore, sets of constraints are required to determine the costs for early and late arrivals; they are omitted here due to space limitations. There are several restriction groups necessary to determine the arrival times of single and combination trips and the resulting deviations from the soft time windows on hourly and daily basis. Furthermore, constraints for not violating the hard time windows and for the maximum trip duration have to be defined. Additionally, there are constraints to guarantee that every customer order is carried out exactly once. Due to space limitations, also these restrictions are not presented here. The following new restriction group (7) guarantees that the maximum number of available trucks of carrier r ∈ R on day t ∈ {1, . . . , T } is not exceeded. t n∈N xnt r + (n,m)∈P ynm r +

yˆ t (n,m)∈PIE nm r 

≤ MaxTruckrt ∀r ∈ R ∧ t ∈ {1, . . . , T } (7)

xnt r = 0∀n ∈ N ∧ r ∈ R ∧ t ∈ {1, . . . , T } : CarTGr = CarTGCarOwnn

(8)

t ynm r =0 ∀(n, m) ∈ P ∧ r ∈ R ∧ t ∈ {1, . . . , T } : CarTGr  = CarTGCarOwnn ∨ CarTGr = CarTGCarOwnm

(9)



t yˆ nm r =0 ∀(n, m) ∈ PIE ∧ r ∈ R ∧ t ∈ {1, . . . , T } : CarTGr  = CarTGCarOwnn ∨ CarTGr = CarTGCarOwnm (10)

The three new restriction groups (8–10) specify that customer orders are only shared within the predefined trust groups. t t , yt As pointed out above, variables xnr nmr and y nmr are defined as binary, the other variables are continuous. The conditions are omitted here, but need to be taken into account in the complete model formulation. It is a particular aim of this work to develop models and mechanisms that render collaboration as fair and thus stable in the long term. Different fairness mechanisms were developed, consisting of one or more constraint groups. These can be used on their own or in combination. Moreover, it is possible either to set α = 1 or to use the new weakened form with α < 1 (see below). Profit Increase Total (PIT) and Profit Increase Day and Total (PIDT): 

t∈{1,...,T } DayProfitrt ≥ α · t∈{1,...,T } pPrt ∀r ∈ R

(11)

DayProfitrt ≥ α · pPrt ∀r ∈ R ∧ t ∈ {1, . . . , T }

(12)

The two constraint groups (11) and (12) are similar to those suggested by Calore et al. (2017). The first specifies that, summed up over all days (i.e., in total), the profit with collaboration must be larger than or equal to the profit without collaboration, i.e. pP tr , for all carriers when α = 1. (With α < 1, it is required that the profit with collaboration exceeds a certain percentage, e.g. 80% (for α = 0.8) of the previous profit.) Restrictions (11) are used for the model variant PIT. The second set of restrictions (12) specifies that

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for each carrier on each day, the profit with collaboration must be larger than or equal to the profit without collaboration. This restriction group is activated for variants PIT and PIDT. (Absolute) Profit Increase Uniformity Total (PIUT) and. (Absolute) Profit Increase Uniformity Day and Total (PIUDT)     DayProfitqt − pPqt   } q∈R t∈{1,...,T t∈{1,...,T } DayProfitrt − pPrt ≥ α ∀r ∈ R |R| (13)    t t   q∈R DayProfitq − pPq t t DayProfitr − pPr ≥ α · ∀r ∈ R ∧ t ∈ {1, . . . , T } (14) |R| These two new restriction groups (13) and (14) are intended to distribute the increase of profit resulting from the collaboration evenly among the carriers (similar to the costbased approach by Uddin and Huynh (2020)) and are used for PIUT (only the first one) and PIUDT (both). Order Number Equality Total (ONET)

t t t + (n,m)∈P 2ynmr + 2ˆynmr − α · kart ≥ 0∀r ∈ R (15) t∈1,...,T n∈N xnr (n,m)∈PIE 

This new constraint group (15) is used for model variant ONET and focuses on customer orders instead of the profit. It guarantees that each carrier performs the same number of orders with collaboratiosn as the carrier originally owned (when α = 1). So there is no change in the number of customer orders for each carrier, but of course it might carry out different orders when collaborating. By using α, this restriction can also be weakened so that only a certain percentage of its original number of orders must be assigned to each carrier.

4 Computational Study and Results To analyze the effects of collaboration under different approaches for establishing fairness, ten test instances were generated for the Hinterland of the port of Hamburg. There are seven carriers, each of which has its own depot in the area of Hamburg where the truck trips start and end. Every carrier belongs to a trust group. Every instance contains 100 customer orders (50 import and 50 export customer orders). Individual customer orders with starting location, end location and desired empty container depot are generated for each instance, based on predefined locations in the hinterland that are identical for all instances. Import customer orders start and export customer orders end at one of the four container terminals in the Port of Hamburg. In the hinterland, there are 20 locations where containers can be loaded or unloaded (e.g. the Volkswagen plant in Wolfsburg) and 12 empty container depots. The distances between the individual locations were determined in length (km) and duration (h) using an online tool (openrouteservice.org). Every customer order contains one container and is assigned to a carrier who originally

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owns this order. Each container can be one out of the above-mentioned four types and is assigned to one of three alliances. The hard time windows are rather wide (e.g. 7:00 to 20:00), but the soft time windows are rather strict (e.g. 12:00 to 15:00). The customer orders’ requested transportation dates are distributed over one week, i.e. 5 working days. Instances differ in terms of the specific customer orders generated. The model was implemented using the Gurobi library and solver for Python and solved on a 3.1 GHz 16-core CPU with 128 GB RAM. At the start, for each instance the solution is determined in which the carriers do not cooperate, but where each carrier can combine its individual customer orders. Two different solutions are developed, one under the assumption that the profit on each day must be higher than the initial profit (without own combinations trips) (RealPriorProfit D + T) and once assuming that only the total profit (of the 5 working days) must be higher than the initial profit (RealPriorProfit T). These calculations are necessary in order to use the calculated profit per carrier as a lower limit in the above-mentioned fairness mechanisms. The five fairness mechanisms are tested with α = 1 and are compared which each other, with the “PriorProfit” cases and with the combination of mechanisms PIT and ONET, as well as with the case without any fairness mechanism (“none”). Furthermore, each is applied to different trust group settings: One big trust group (1TG) where all seven carriers can share customer orders, two trust groups (2TG) with three or four carriers each and three trust groups (3TG) with two or three carriers each. In Fig. 2, the average profit of all ten instances is given, with the profit indexed relative to the base case (RealPrior Profit T), the y-axis truncated for readability, and the data label given for the 1TG case. Figure 2 shows that through collaboration total profit increases by up to 35%. However, the increase in profit largely depends on the fairness mechanism and the trust group splitting. Not surprisingly, the fairness mechanisms that only strive for fairness on a weekly basis (PIT and PIUT) show a stronger increase in profits than those that also enforce an increase in profits on a daily basis (PIDT and PIUDT) due to the higher flexibility of the former. This effect is very obvious for PIUT in particular compared to PIUDT. It also becomes obvious that taking into account a fair distribution of the profit increase as it is the case in PIUT and PIUDT leads to a significant profit reduction compared to PIT/PIDT where only the individual prior profit is considered. While ONET by definition does not involve profit, but the number of customer orders, an average profit increase is still possible with this approach. In the case of PIT and ONET combined, total profit is lower compared to PIT or ONET individually (as expected), but the difference to ONET is small. When no fairness restriction is applied (“none”), the average profit increase is highest, but collaboration can be expected to be least stable in that case. Regarding trust groups, it can be seen that - as expected - the larger the trust group, i.e. the more carriers can exchange customer orders, the larger is the increase in profit. So, the loss in profit occurring due to lack of trust is considerable. However, it should be emphasized that even three small trust groups (3TG) allow for a recognizable increase in profit. This means that even if only two or three carriers trust each other and exchange customer orders, they can achieve a significant advantage.

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Total profit

Comparison of profit 140% 130% 120% 110% 98% 100% 90% 80%

133% 130%

128% 116% 99%

135% 124% 121%

100%

Fairness mechanism

1TG

2TG

3TG

Fig. 2. Comparison of profits for different fairness mechanisms and trust group sizes.

Furthermore, the results show that the profit already slightly increases if each carrier only optimizes the transport of its own customer orders (from Prior Profit to RealPrior Profit). This is due to the fact that while in the PriorProfit option all customer orders are carried out as single trips, in the RealPriorProfit option combination trips (of the individual carrier’s orders) are possible. However, out of 100 customer orders, only eight customer orders are combined into four combination trips on average in this case. It turns out that the number of combination trips depends mainly on the trust group size. At 1TG, for example, an average of 30 customer orders (across all fairness mechanisms) are executed in combination trips (including 6 with street turn), and at 3TG on average only 16 customer orders are executed in combination trips (including approx. 3 with street turn). To compare the sharing and forwarding of customer orders in more detail, Senkey diagrams are used. The widths of the respective lines and bars indicate the revenues of the customer orders. Figure 3 shows the sharing of customer orders in a 1TG setting with the fairness mechanism PIT. It shows on the left-hand side the seven carriers with their own customer orders. On the right-hand side, the distribution of customer orders with collaboration can be seen. It is clearly visible that carriers 4 and 6 perform many customer orders when collaboration is in place. In contrast to this, carrier 1 ends up with one single

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Carrier 1

Carrier 2

Carrier 3

Carrier 2 Carrier 3

Carrier 4

Carrier 4 Carrier 5 Carrier 5 Carrier 6

Carrier 6

Carrier 7 Carrier 7

Fig. 3. Senkey diagram of an example instance for one trust group (1TG) and mechanism PIT (left-hand side without cooperation, right-hand side with cooperation).

customer order. Carrier 1 receives high compensation payments for giving away its own customer orders, resulting in more profit than without collaboration. However, this is clearly not a sustainable collaboration for this carrier, as it has no customer contact anymore and does not make use of its truck fleet and drivers. Therefore, it seems advisable to combine two fairness mechanisms, as for example PIT and ONET. Figure 4 shows the resulting effect for the same instance. From this Senkey diagram it is obvious that in this case the number of orders in the collaboration setting is far more evenly distributed than before. However, e.g. carrier 7 in this solution does no longer execute any of its own customer orders, but serves other orders which are more suitable for it. In summary, the combination of the mechanisms PIT and ONET leads to more stable collaboration than it is the case with PIT alone. However, through combining these two mechanisms the total profit is slightly diminished compared to the “PIT only” case, as shown in Fig. 2. This shows the conflict between the two objectives to establish a fair and potentially long-term stable collaboration and to achieve the highest possible short-term advantage (here: profit) through the collaboration, but also that the combination of PIT and ONET leads to a good compromise.

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Carrier 1

159

Carrier 1 Carrier 2

Carrier 2 Carrier 3

Carrier 3

Carrier 4

Carrier 4 Carrier 5 Carrier 5 Carrier 6 Carrier 6 Carrier 7

Carrier 7

Fig. 4. Senkey diagram of an example instance for one trust group (1TG) and PIT combined with ONET (left-hand side without cooperation, right-hand side with cooperation).

5 Conclusion The aim of this paper is to develop a new practice-oriented model for the operative optimization of container transportation in the port hinterland, with a focus on establishing fair and long-term stable collaboration among carriers. It is shown that through implementing and combining the aspects of compensation payments, trust groups and fairness mechanisms, realistic collaboration settings can be achieved which improve overall profit and support long-term stable collaboration due to a fair sharing of orders and profits. Moreover, it turns out that in the very competitive market of hinterland container transportation by truck even the sharing of customer orders between only two or three carriers which trust each other has a significant positive impact on their profit. Therefore, a managerial implication of this work is that starting horizontal collaboration with some friendly and trustworthy competitors on a small scale might be better than waiting for a broad market collaboration solution. The positive results that can be expected to be achieved through collaborating with a “trust group” may also improve the currently predominantly negative attitudes towards collaboration (Reinecke et al., 2023) and hopefully will contribute to further spreading collaboration in this field. However, the model as well as the results have some limitations. First, the actual profit increase of collaboration depends on the carriers’ cost structure and customer orders. The data sets used in this work were artificially generated and while they are based on interviews and market research regarding cost rates and revenue opportunities, they still were not drawn from actual company data. Hence, studies with real data should be carried out in the future to confirm the results in practical settings. Second, uncertainty with respect to travel times or customer demand is not incorporated in the model, which could

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also be considered in the future. Third, although interviewees asserted that emissions reduction has very little relevance in practice (own study), an analysis of how emissions reduction can be achieved through collaboration is another fruitful and important future field of research.

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Luo, T., Chang, D., Xu, Z.: Forwarder’s empty container ordering and coordination considering option trading in the container transportation service chain. CAIE 156(3), 107251 (2021) Notteboom, T.E., Rodrigue, J.-P.: Port regionalization: towards a new phase in port development. MPM 32(3), 297–313 (2005) Notteboom, T.E., Rodrigue, J.-P.: Containerisation, box logistics and global supply chains: the integration of ports and liner shipping networks. Marit. Econ. Logist. 10(1), 152–174 (2008) Notteboom, T.E., Rodrigue, J.-P.: The future of containerization: perspectives from maritime and inland freight distribution. GeoJournal 74(1), 7–22 (2009) Pan, S., Trentesaux, D., Ballot, E., Huang, G.Q.: Horizontal collaborative transport: survey of solutions and practical implementation issues. Int. J. Prod. Res. 57(15–16), 5340–5361 (2019) Pomponi, F., Fratocchi, L., Tafuri, S.R.: Trust development and horizontal collaboration in logistics: a theory based evolutionary framework. Supply Chain Manag. 20(1), 83–97 (2015) Reinecke, P.C., Wrona, T., Rückert, N., Fischer, K.: A typology of the collaboration willingness of carriers in hinterland logistics. IJPDLM. ahead-of-print (2023) Sanchez Rodrigues, V., Harris, I., Mason, R.: Horizontal logistics collaboration for enhanced supply chain performance: an international retail perspective. Supply Chain Manag. 20(6), 631–647 (2015) Serrano-Hernández, A., Juan, A.A., Faulin, J., Perez-Bernabeu, E.: Horizontal collaboration in freight transport: concepts, benefits and environmental challenges. SORT 1(2), 393–414 (2017) Song, D.-P., Carter, J.: Empty container repositioning in liner shipping. MPM 36(4), 291–307 (2009) Song, D.-P., Dong, J.-X.: Empty container repositioning. In: Lee, C.-Y., Meng, Q. (eds.) Handbook of Ocean Container Transport Logistics. ISORMS, vol. 220, pp. 163–208. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11891-8_6 Sterzik, S., Kopfer, H., Yun, W.-Y.: Reducing hinterland transportation costs through container sharing. Flex. Serv. Manuf. J. 27(2–3), 382–402 (2015). https://doi.org/10.1007/s10696-0129167-y Theofanis, S., Boile, M.: Empty marine container logistics: facts, issues and management strategies. GeoJournal 74(1), 51–65 (2009) Tran, N.K., Haasis, H.-D., Buer, T.: Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission. Marit. Econ. Logist. 19(4), 667–694 (2017) Uddin, M., Huynh, N.: Model for collaboration among carriers to reduce empty container truck trips. Information 11(8), 377 (2020) Vanovermeire, C., Sörensen, K., Van Breedam, A., Vannieuwenhuyse, B., Verstrepen, S.: Horizontal logistics collaboration: decreasing costs through flexibility and an adequate cost allocation strategy. Int. J. Logist. Res. Appl. 17(4), 339–355 (2014) Widjanarka, A., Wirjodirdjo, B., Pujawan, N., Baihaqi, I.: Coalition in utilization capacity in container transportation services. IJASE 15(2), 95–104 (2018)

Supply Chain Operations

Lot Streaming in Hybrid Flow Shop Manufacturing Systems Janis S. Neufeld1(B) , S¨ ohnke Maecker3 , Liji Shen3 , Rub´en Ruiz2 , and Udo Buscher1 1

Faculty of Business and Economics, Technische Universit¨ at Dresden, 01069 Dresden, Germany {janis sebastian.neufeld,udo.buscher}@tu-dresden.de 2 Department of Applied Statistics, Operations Research and Quality, Universitat Polit`ecnica de Val`encia, Camino de Vera s/n, Edificio 7A, 46022 Valencia, Spain [email protected] 3 Chair of Operations Management, WHU – Otto Beisheim School of Management, 56179 Vallendar, Germany {soehnke.maecker,liji.shen}@whu.edu Abstract. In traditional machine scheduling, the focus is on determining the sequence of jobs on the machines. Meanwhile, the logistical question of how to organize the transport process between production stages frequently remains in the background. In this article, however, we consider lot streaming, offering the possibility to forward units to the next production stage before the entire production lot has been completed. Specifically, we analyze lot streaming in a hybrid flow shop where the aim is to minimize the makespan. For this purpose, two mixed-integer optimization models are developed and compared with respect to their solution quality. Based on the superior formulation, we analyze the influence of the configuration of the hybrid flow shop in terms of the number of production stages, the number of machines on the stages, and the length of the processing times on the makespan for moderate problem sizes. The results obtained highlight the importance and effect of different lot streaming scenarios in scheduling, and, in particular when the increased complexity of scheduling with sublots is worthwhile.

1

Introduction

The hybrid flow shop scheduling problem (HFSP) is of high practical relevance in today’s manufacturing systems (Neufeld et al., 2022). In contrast to pure flow shops, in the HFSP multiple parallel machines are available on at least one stage. By including this extension, bottlenecks can be alleviated. At the same time, parallel machines increase the complexity of the underlying scheduling problem since the assignment of jobs to the parallel machines has to be considered as well. Hence, various solution approaches have been proposed for solving the HFSP (Ruiz and V´ azquez-Rodr´ıguez, 2010; Tosun et al., 2020). In classical scheduling, jobs are usually assumed to be indivisible and transport processes between the production stages are neglected (Buscher and Shen, c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 165–179, 2023. https://doi.org/10.1007/978-3-031-38145-4_10

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2009). However, often jobs in fact consist of a number of separable items. By splitting jobs into smaller sublots and explicitly considering individual transports of the sublots to subsequent stages, lot streaming techniques are able to accelerate production processes significantly (Cheng et al., 2013). This idea adds a new degree of freedom to the planning problem. For each job, the number of sublots as well as their sizes have to be determined. Furthermore, sublots may be consistent for each job, i.e., their number and size does not change during the production process. Variable sublots relax this constraint and enable a composition of sublots on subsequent stages. In the case of parallel machines, it may be possible to process two sublots of a job on different machines. To further increase flexibility, intermingling allows a mixture of sublots of different jobs in the processing sequence. These characteristics again increase the complexity of the underlying planning problem, while at the same time offering additional potential for improvement to the production process. In this work, we integrate the two aforementioned aspects and study the HFSP with lot streaming. Two linear mixed-integer programs (MIP) are formulated and compared regarding their suitability to solve the HFSP with lot streaming. More precisely, we include important assumptions to reduce model size, and define different decision variables to improve model performance. Also, the MIP formulations are used to analyze the potential of lot streaming in hybrid flow shops. The main objective is to analyze the trade offs between improvements in scheduling performance (makespan) and the added complexity that results from different lot streaming techniques. The remainder of the paper is structured as follows. Section 2 presents the existing literature on the HFSP with lot streaming, which is followed by the detailed problem definition in Sect. 3.1. The two MIP formulations are described in Sect. 3.2. Section 4 presents the results of our computational study. These include a comparison of the two formulations and analyses of the impact of lot streaming in the HFSP. Finally, Sect. 5 closes the paper with a summary and an outlook to future research.

2

Related Literature

A review of lot streaming in different environments can be found in Cheng et al. (2013), which considers publications up to 2012. More recently, Salazar-Moya and Garcia (2021) summarizes lot streaming applications to minimize makespan. But only limited research has been conducted on lot streaming in hybrid flow shops. The first studies on lot streaming in hybrid flow shops focus on simplified environments. Kim et al. (1997) was the first to introduce lot streaming in twostage HFSP with equal sublots and makespan objective. The authors use a modified Johnson’s rule to determine optimal solutions. Zhang et al. (2005) and Liu (2008) present special cases with parallel machines on the first stage only. In contrast, Cheng and Sarin (2013) analyzes a hybrid flow shop with a single machine on the first stage and two parallel machines on the second stage. For these specialized environments several properties are derived and heuristic and optimizing

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methods proposed. More recently, Wang et al. (2019) proposes a linear MIP and heuristics for a two-stage HFSP with integrated batching and lot streaming to minimize total weighted completion time, i.e., the sum of all completion times is weighted according to the importance of each job. The more general multi-stage HFSP with lot streaming is studied in this paper. For this problem, consistent sublots and the objectives makespan or total (weighted) completion time are commonly considered. Defersha and Chen (2012) presents an MIP and parallel genetic algorithm minimizing makespan. Here, unrelated parallel machines, an intermingling of sublots from different jobs and a skipping of stages are considered. For a small numerical example, a significant effect of lot streaming on makespan is analyzed and compared to a pure flow shop environment. However, the analysis is not generalized for a larger number of instances. Similarly, Nejati et al. (2014) minimizes total weighted completion time using a genetic algorithm, that determines a job sequence as well as sublot sizes. Zhang et al. (2017) addresses the HFSP with equal sublots and the objective of minimizing the total flow time. For this problem, a migrating birds algorithm is developed. Lalitha et al. (2017) studies an HFSP where parallel machines are only present in the last stage. For this special case a two-stage heuristic is developed that first splits the jobs and subsequently determines a sequence. It is proven that regarding makespan the heuristic is able to find nearoptimal solutions. Zhang et al. (2021) minimizes makespan taking transportation times for the lots into consideration. For this, a variable neighborhood descent algorithm is developed. Other objectives are seldom studied. Naderi and Yazdani (2014) proposes an MIP formulation and an imperialist competitive algorithm to minimize total tardiness. In contrast to most other studies, only equal sublots are allowed. Multi-objective problems are considered by Chen et al. (2020) and Zhang et al. (2022). Chen et al. (2020) uses NSGA-II to minimize makespan as well as the maximum power consumption, while allowing intermingling of sublots. Besides makespan, Zhang et al. (2022) regards the number of sublots as second objective in an evolutionary algorithm. Even four objectives are taken into account by Li et al. (2020), i.e., energy consumption, sojourn times, earliness, and tardiness. In summary, publications are still limited and lack a quantification of the potential of lot streaming in hybrid flow shop environments, which is the main goal of this paper.

3 3.1

Problem Description and Mathematical Formulation Problem Definition

We consider the hybrid flow shop problem (HFSP) where the lot streaming technique is applied. Given are a set of n jobs (j = 1, . . . , n) and a set of m stages (i = 1, . . . , m). All jobs are available at time zero and pass through the stages in the same technological order. Each stage i consists of pmi parallel identical machines (k = 1, . . . , pmi ). Furthermore, we assume that a job j contains multiple identical items qj . The entire job of size qj is allowed to be divided into

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smaller parts known as sublots. The unit processing time of job j on stage i is given by pij which is equal to the processing time of a single item. The total processing time can thus be expressed as pij ·qj . Once a job j is split into sublots, they can be assigned to any of the identical machines k available on a stage i. However, all sublots of a job have to be processed on the same machine. Our main purpose is to examine the impact of lot streaming on the makespan of the complete sequence Cmax . Subject to the traditional assumptions for HFSP, the following constraints apply when considering lot streaming: 1. A job j can be divided into non-preemptive equal sublots l of size olj (l = 1, . . . , L), with L being the number of sublots. 2. A number of sublots L is given and constant for all jobs. Therefore, the L  condition olj = qj holds for all jobs (j = 1, . . . , n). l=1

3. Sublot sizes remain consistent through all stages (i = 1, . . . , m). 4. Intermingling of jobs is not allowed. Once a job j is started on a machine, all sublots of job j must be assigned to the same machine and must be completed on this machine before the processing of the next job. 5. Batch availability applies where a sublot becomes available and can be transferred to the next stage once the last item of this sublot is completed. 3.2

Mathematical Models

In this section, we present two mathematical models for the HFSP with lot streaming. First, we introduce the following binary variable  xikjj  =

1, if job j is processed directly before j  on the kth machine of stage i, 0, otherwise,

(1) Other variables are the start time tijl , completion time Cijl of the lth sublot of job j on stage i, and the objective makespan Cmax . Furthermore, we introduce two dummy jobs 0 and n + 1, which mark the start and the end of the schedule on each machine. The total processing time for each sublot on a specific stage is given by Pijl = pij qj /L. With H representing a sufficiently large number, our problem can thus be formulated as follows (MIP1): min Cmax

(2)

subject to pmi n  

xikj  j = 1 ∀j, i

(3)

j  =0 k=1 j  =j n  j  =0 j  =j

xikj  j =

n+1  j  =1 j  =j

xikjj 

∀j, i, k

(4)

Lot Streaming in Hybrid Flow Shop Manufacturing Systems

Cijl ≥ tijl + Pijl ∀l, j, i ti+1jl ≥ Cijl ∀l, j, i < m tijl+1 ≥ Cijl ∀j, i, l < L tijl ≥ Cij  L − H (1 − xikj  j ) Cmax ≥ CmjL ∀j

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(5) (6) (7) ∀j = j  , i, k

(8) (9)

tijl ≥ 0 ∀i, j, l xikjj  ∈ {0, 1} ∀i, k, j, j 

(10) (11)

Equations (3) require that each job is assigned to one and only one machine on each stage. Combining with (4), each job has exactly one preceding and one succeeding job on the assigned machine. The completion time of each sublot is determined by constraints (5). Constraints (6) formulate the flow shop environment while constraints (7) require a successive processing of all sublots of the same job. Constraints (8) prevent overlapping of jobs on machines. At the same time, they require non-intermingling of the jobs on the machines. According to constraints (9), the makespan is defined by the maximum completion time of the last sublots. Constraints (10) and (11) define the decision variables. Alternatively, we define two additional binary variables below:  1, if job j is processed by the kth machine on stage i, (12) yikj = 0, otherwise, 

and zijj  =

1, if job j precedes job j  on stage i, 0, otherwise.

(13)

We next propose the second formulation (MIP2): min Cmax

(14)

subject to pmi 

yikj = 1

∀i, j

(15)

k=1

Cijl ≥ tijl + Pijl ∀i, j, l ti+1,jl ≥ Cijl ∀j, l, i < m tijl+1 ≥ Cijl ∀i, j, l < L tij1 ≥ Cij  L − H (3 − yikj − yikj  − zij  j ) tij  1 ≥ CijL − H (2 − yikj − yikj  + zij  j ) Cmax ≥ CmjL ∀j tijl ≥ 0 ∀i, j, l yikj , zijj  ∈ {0, 1}

∀i, k, j, j  , l

(16) (17) 

∀i, j < j , k ∀i, j < j  , k

(18) (19) (20) (21) (22) (23)

The objective is to minimize the makespan (14). Equations (15) ensure that each job is assigned to one and only one machine on each stage. Constraints (16)– (18) are equivalent to (5)–(7) in MIP1. Disjunctive constraints (19) and (20)

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handle the calculation of completion times with respect to the job sequence on the stages, ensure the non-intermingling requirement, and prevent overlapping on machines. The decision variables are defined by (22) and (23).

4 4.1

Computational Study Experimental Setting

We use MIP1 and MIP2 presented in Sect. 3.2 to conduct a computational study for small problem instances to investigate the effect of lot streaming on the makespan. Furthermore, the relation between the different instance parameter settings and the potential makespan improvement is of interest to this study. Test instances were randomly generated according to the settings presented in Table 1. In our main experiment, we examined 800 different configurations, for each of which we generated 10 distinctive instances with random processing times from the given range. Hence, 8000 instances were tested in total. The sublot processing times Pijl are determined by pij · 60/L for each job j. This can be interpreted as a conversion of minutes to seconds. Table 1. Test Instance Configurations Factor

Levels

Count

Stages m Parallel machines per stage pmi Jobs n Sublots L Processing time pij

m ∈ {2, 3, 4, 5, 6} pmi ∈ {1, 2, 3, 4} n ∈ {6, 8, 10, 12} L ∈ {1, 2, 3, 4, 6} pij ∈ {U (1, 100), U (80, 100)}

5 4 4 5 2

Total Number of Configurations

800

The MIPs are implemented both in Gurobi and CPLEX using the C++ APIs of Gurobi v8.1 and CPLEX v12.10. The computing time limit was set at 3600 s seconds with a maximum of four threads to be used. All tests were conducted on an Intel Xeon CPU E5-2697 v3 computer with 2.60 GHz and 128 GB RAM. 4.2

Preliminary Tests

In our preliminary tests, we compare the performance of MIP1 and MIP2. The total numbers of variables and constraints are first determined for both models. Table 2 reports the model sizes for the corresponding problem instances. We can see that MIP2 has comparatively fewer variables and constraints if parallel machines (pmi = 1) and sublots (L = 1) are available. In our main experiment, the maximum number of jobs was limited to 12 for computational considerations. Here, we also tested instances with up to 15

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Table 2. Model sizes Instance configuration MIP1 MIP2 No. of variables No. of constraints No. of variables No. of constraints {m, pmi , n, L} {2,1,6,1} {6,1,6,1} {3,2,8,2} {2,4,12,6} {4,3,10,4} {5,4,12,4} {6,4,12,6}

97 289 481 1441 1521 3361 4321

120 360 544 1500 1650 3624 4620

109 325 337 673 841 1441 2017

108 324 496 1404 1530 3384 4332

jobs. As shown in Table 3 and Fig. 1, MIP2 outperforms MIP1 in terms of both computational time and solution quality. For example, for instances with 10 jobs, MIP2 can find an optimal solution within the time limit for 94 % of the instances, while MIP1 can solve only 75 %. Also, Gurobi seems to reach optima more quickly compared to CPLEX. We calculate the relative percentage deviation (RP D) from the makespan Cmax of a solution by MIP1 / MIP2 to the bestbest . found solution of both formulations Cmax RP D =

best Cmax − Cmax best Cmax

(24)

Figure 1 shows the average RPD (ARPD) over the respective set of test instances. It can be seen that MIP2 finds the best solution for nearly all instances, while MIP1 solutions average up to 7% worse for instances with n = 15. We therefore use only MIP2 with Gurobi in our main experiments. Moreover, because the small number of optimally solved problems do not allow a proper analysis of the potential of lot streaming, problems with n = 15 jobs are not considered in the following. Table 3. Rate of Problems Solved to Optimality by MIP1 and MIP2 Using Gurobi and CPLEX n

Gurobi CPLEX MIP1 MIP2 MIP1 MIP2

6 100% 100% 100% 8 100% 100% 96% 10 75% 94% 59% 15 4% 34% n.a.

100% 99% 85% n.a.

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Fig. 1. Means plot of ARPD with 95% confidence intervals (left) and box plot of computing time (right) by MIP1 and MIP2 implemented in Gurobi depending on n.

Fig. 2. Lot streaming in a two-stage flow shop

4.3 4.3.1

Main Experiments Illustrative Examples

To gain a first impression of lot streaming, we first show detailed optimal solutions to exemplary problem instances. In each schedule, a critical path is highlighted with red color. The critical path includes only sublots that cannot be started later without increasing the makespan, i.e., these sublots determine the makespan. Note, that there might be more than one critical path in the respective schedules. Figure 2 depicts a classical flow shop problem of two stages without parallel machines and six jobs. The upper Gantt-chart shows a solution without lot streaming while two sublots are used for each job in the lower chart. We can see that there is a relevant but limited improvement potential. Lot streaming with two equal sublots results in around 6% lower makespan. Figure 3 compares the optimal schedules of a HFSP with eight jobs and three stages. On each stage, there are four parallel machines. When adopting four sublots for each job, the makespan is reduced by 28 %. The initial idle times on stages two and three decrease considerably and the machines on these stages can

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Fig. 3. Lot streaming in a HFS

start much earlier with processing. The example shows a significant improvement with the introduction of lot streaming to HFSP.

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4.3.2

Effect of Lot Streaming in HFSP

For a precise evaluation of the effect of lot streaming, we solve all 8,000 small- to medium-sized test instances using MIP2 with different fixed numbers of sublots L. We determine the relative percentage makespan improvement (RP I) based on the number of sublots:   Cmax (L) RP I = 1 − × 100, (25) Cmax (L = 1) where Cmax (L) is the makespan of the schedule with L equal sublots for each job. Figure 4 shows RP I values with respect to the individual instance parameter setting for the number of sublots L, the number of stages m, the range of processing times (either pij ∈ [1, 100] or pij ∈ [80, 100]), and the number of parallel machines pmi . As can be seen, RP I increases with the numbers of sublots, stages, as well as the number of machines on each stage. However, for all three parameters the marginal improvement decreases with increasing parameter values. Note that optimal solutions are not found for some instances, which may underestimate the potential of lot streaming. With a total RP I of up to 29% for six sublots compared to the HFSP without lot streaming (L = 1), lot streaming can lead to a considerable reduction of makespan. The biggest impact with about 18% RP I can be gained if the number of sublots is increased from one to two, i.e., lot streaming is introduced with a splitting of jobs into two sublots. For a change from four to six sublots, however, the additional improvement is only about 3%. Considering the relationship between the RP I and L, in practical manufacturing systems, decision-makers can balance the additional improvement and the higher complexity that comes with a higher number of sublots accordingly. In addition, RP I is larger if processing times are drawn from [80, 100] instead of [1, 100], i.e., if processing times are rather similar for all jobs. This is reasonable since job splitting does not have such a high impact for jobs with very low processing times compared to large processing times. Furthermore, it is found that lot streaming is particularly beneficial in large shop floors, with a high number of stages and parallel machines. All differences are statistically significant. In addition, the charts in Fig. 5 investigate the interaction between the total number of sublots L and the number of stages, the range of processing times, as well as the number of parallel machines. The latter are all parameters that describe important aspects of the problem setting and can indicate whether lot streaming is only beneficial for specific parameter configurations or for HFSP in general. For all analyzed combinations, a strong interaction can be observed. This means that the specific parameters indeed have an significant impact on the benefits of lot streaming. For example, for L = 6, the RP I increases from about 13 % for 2 stages to nearly 40 % for 6 stages. However, for all settings the observations suggest that lot streaming is beneficial in general. Despite higher solution complexity, more sublots are justified, especially for larger hybrid flow shop systems with a high number of stages and parallel machines. In contrast, as shown in Fig. 6, benefits of lot streaming appear to decline when the total number of jobs increases. It is understandable that lot streaming

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Fig. 4. Means plot of RPI with 95% Tukey Honest Significant Difference (HSD) confidence intervals based on the number of lots L, number of stages m, range of processing times, and number of parallel machines per stage pm.

can exploit the improvement potential particularly when only a single job is to be considered. In the presence of a large number of jobs, job interactions can limit the impact of lot streaming. Nevertheless, with an average improvement of more than 15.5 % for the largest tested instances (n = 12), the effect is still impressive. With increasing n, the additional marginal improvement of makespan decreases. However, this may also be related to the fact that larger HFSP instances with n > 12 cannot be solved to optimality using MIP2, and thus cannot be included in the analyses.

Fig. 5. Means plot of RPI with 95% Tukey Honest Significant Difference (HSD) confidence intervals based on different instance parameters interacting with the number of sublots L.

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Fig. 6. Means plot of RPI with 95% Tukey Honest Significant Difference (HSD) confidence intervals and box plot based on n interacting with the number of sublots L.

4.3.3

Impact of Alternative Sequencing

We are also interested in the impact of the sequence of jobs on the makespan improvement for L > 1. This can be helpful to asses if an integrated planning of the sequencing problem and the lot streaming problem is worthwhile. To investigate this, in a second set of tests, the sequence and machine assignment of the best found solution with L = 1 was recorded and kept for subsequent solver runs with L > 1. We refer to this as static sequence. The solution of the problem found by MIP2 with L > 1 without this predefined job sequence is called dynamic sequence. Afterwards, we compare the RP Id of solutions with dynamic sequence to the RP Is of solutions with static sequence. We measure the difference by Δ = RP Id − RP Is . It can be viewed as the potential loss for keeping the optimal sequence and assignment of the problem without fixed sublots. Results are sorted by the instance parameters in Table 4. For all settings, Δ increases moderately with an increasing number of sublots L, number of stages m, and number of parallel machines pm. However, regarding L and m, the share of improvement that can be gained by a dynamic sequence, Δ/RP Id , remains relatively constant. The number of jobs n has nearly no impact on the absolute values of Δ, i.e., the absolute improvement of determining a new sequence and machine assignment when sublots are introduced remains constant with increasing numbers of jobs. This is especially relevant because the number of jobs is a major complexity driver in scheduling problems. Nevertheless, it must be noted that Δ is measured based on the absolute values of RP I. This means that a relatively constant value of Δ, still results in an increasing relative improvement of RP Is compared to RP Id for larger n. The impact of the range of processing times is striking. Subject to a small range of processing times with pij ∈ U (80, 100), optimal or near-optimal solutions can be found for a majority of instances with the given static sequence. These results underline the importance of the distribution of processing times, which is often not addressed explicitly in scheduling research (see also Watson et al., 2002).

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Table 4. RP I-difference between solutions with dynamic and static sequences based on instance parameters. L

2

3

4

6

n

RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

6 8 10 12

22.04 18.86 16.19 14.69

28.88 24.65 21.15 19.21

32.16 27.48 23.58 21.44

35.36 30.25 26.01 23.63

m

RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

2 3 4 5 6

7.97 14.72 19.33 22.58 25.11

10.38 19.23 25.34 29.55 32.88

11.55 21.43 28.18 32.95 36.72

12.69 23.58 30.99 36.30 40.51

p

RP Id RP Is Δ

1 80

16.27 10.34 5.93 20.88 12.88 8.00 23.05 14.07 8.98 25.14 15.23 9.91 19.62 19.27 0.36 26.07 25.59 0.48 29.28 28.74 0.54 32.49 31.89 0.60

18.97 15.63 12.94 11.67 6.84 12.03 15.67 18.58 20.88

3.07 3.22 3.26 3.02 1.12 2.69 3.66 4.00 4.23

24.59 20.31 16.81 15.23 8.88 15.58 20.34 24.17 27.20

4.29 4.35 4.35 3.98 1.50 3.66 5.00 5.38 5.68

RP Id RP Is Δ

27.35 22.58 18.72 16.98 9.89 17.31 22.63 26.91 30.30

4.81 4.90 4.87 4.46 1.65 4.12 5.55 6.04 6.42

RP Id RP Is Δ

30.07 24.84 20.61 18.71 10.88 19.03 24.90 29.61 33.37

5.29 5.41 5.40 4.92 1.81 4.55 6.09 6.69 7.14

RP Id RP Is Δ

pm RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

RP Id RP Is Δ

1 2 3 4

12.56 21.46 27.54 32.34

14.06 23.97 30.69 35.94

15.54 26.43 33.83 39.45

9.51 16.32 21.09 24.85

8.62 13.67 16.83 20.08

0.90 2.65 4.26 4.76

11.30 17.82 21.88 25.94

1.27 3.64 5.66 6.40

12.62 19.85 24.36 28.80

1.44 4.12 6.33 7.14

13.93 21.88 26.81 31.61

1.61 4.55 7.02 7.84

In summary, about 75% to 90% of the RP I by lot streaming, can also be gained if the optimal sequence and machine assignment without lot streaming is kept. However, this share varies, especially with respect to the spread of processing times. Especially for large numbers of sublots L, it may be beneficial to decompose the complex HFSP with lot streaming into two separate planning tasks that can be solved iteratively. First, the sequencing and assignment of jobs to machines could be considered as an HFSP without lot streaming. The number of sublots could be determined in a second step for the gained solution. For both sub-problems, efficient algorithms are already available, and our results indicate that this iterative procedure may lead to good results. However, detailed analyses are necessary to verify this hypothesis.

5

Conclusions

In this paper, we consider applying lot streaming in the hybrid flow shop system, where the objective is to minimize the makespan. To investigate the benefits of lot streaming, two mathematical models are proposed by defining different decision variables. After identifying the better-performing MIP in our

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preliminary tests, we conduct major experiments on randomly generated problem instances. Subject to various parameter combinations, computational results confirm the remarkable improvements achieved by lot streaming. This is especially the case in settings with a large number of stages and a high number of parallel machines. The results can provide guidelines for decision-makers to which extent the increased complexity by introducing sublots is worthwhile. Due to the high complexity of the HFSP, using MIPs can only solve small problem instances to optimality. However, the improvement in makespan encourages us to tackle large real-life instances with appropriate approaches. Moreover, analyzing the optimal solutions generated by MIPs may inspire and facilitate the development of heuristic approaches. Particularly for settings with a tight range of processing times, these approaches could be based on a decomposition of the HFSP with lot streaming.

References Buscher, U., Shen, L.: An integrated tabu search algorithm for the lot streaming problem in job shops. Eur. J. Oper. Res. 199(2), 385–399 (2009) Chen, T., Cheng, C., Chou, Y.: Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming. Ann. Oper. Res. 290(1), 813–836 (2020) Cheng, M., Mukherjee, N., Sarin, S.: A review of lot streaming. Int. J. Prod. Res. 51(23–24), 7023–7046 (2013) Cheng, M., Sarin, S.: Two-stage, multiple-lot, lot streaming problem for a 1+ 2 hybrid flow shop. IFAC Proc. Vol. 46(9), 448–453 (2013) Defersha, F., Chen, M.: Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem. Int. J. Adv. Manuf. Techno. 62(1), 249–265 (2012) Kim, J., Kang, S., Lee, S.: Transfer batch scheduling for a two-stage flowshop with identical parallel machines at each stage. Omega 25(5), 547–555 (1997) Lalitha, J., Mohan, N., Pillai, V.: Lot streaming in [n-1](1)+n (m) hybrid flow shop. J. Manuf. Syst. 44, 12–21 (2017) Li, J., et al.: Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots. Swarm Evol. Comput. 52, 100600 (2020) Liu, J.: Single-job lot streaming in m- 1 two-stage hybrid flowshops. Eur. J. Oper. Res. 187(3), 1171–1183 (2008) Naderi, B., Yazdani, M.: A model and imperialist competitive algorithm for hybrid flow shops with sublots and setup times. J. Manuf. Syst. 33(4), 647–653 (2014) Nejati, M., Mahdavi, I., Hassanzadeh, R., Mahdavi-Amiri, N., Mojarad, M.: Multijob lot streaming to minimize the weighted completion time in a hybrid flow shop scheduling problem with work shift constraint. Int. J. Adv. Manuf. Technol. 70(1), 501–514 (2014) Neufeld, J., Schulz, S., Buscher, U.: A systematic review of multi-objective hybrid flow shop scheduling. Eur. J. Oper. Res. 309(1), 1–23 (2022) Ruiz, R., V´ azquez-Rodr´ıguez, J.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205(1), 1–18 (2010) Salazar-Moya, A., Garcia, M.: Lot streaming in different types of production processes: a Prisma systematic review. Designs 5(4), 67 (2021)

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¨ Marichelvam, M., Tosun, N.: A literature review on hybrid flow shop schedulTosun, O., ing. Int. J. Adv. Oper. Manag. 12(2), 156–194 (2020) Wang, S., Kurz, M., Mason, S., Rashidi, E.: Two-stage hybrid flow shop batching and lot streaming with variable sublots and sequence-dependent setups. Int. J. Prod. Res. 57(22), 6893–6907 (2019) Watson, J., Barbulescu, L., Whitley, L., Howe, A.: Contrasting structured and random permutation flow-shop scheduling problems: search-space topology and algorithm performance. INFORMS J. Comput. 14(2), 98–123 (2002) Zhang, B., Pan, Q., Gao, L., Zhang, X., Sang, H., Li, J.: An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming. Appl. Soft Comput. 52, 14–27 (2017) Zhang, B., Pan, Q., Meng, L., Lu, C., Mou, J., Li, J.: An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots. Knowl.-Based Syst. 238, 107819 (2022) Zhang, B., et al.: A collaborative variable neighborhood descent algorithm for the hybrid flowshop scheduling problem with consistent sublots. Appl. Soft Comput. 106, 107305 (2021) Zhang, W., Yin, C., Liu, J., Linn, R.: Multi-job lot streaming to minimize the mean completion time in m-1 hybrid flowshops. Int. J. Prod. Econ. 96(2), 189–200 (2005)

Carbon-Efficient Scheduling in Distributed Permutation Flow Shops An Analysis of Cause-Effect Relationships Martin Sch¨ onheit(B) Faculty of Business and Economics, Technische Universit¨ at Dresden, 01187 Dresden, Germany [email protected] Abstract. A critical challenge increasingly becoming part of the dayto-day industry business is reconciling competitiveness and profitability with the sustainability of industrial value creation. With the increased frequency of natural disasters caused by climate change, customer awareness of sustainability is changing. Additionally, governments are increasingly taking regulatory action to limit harmful effects of climate change. Hence, the sustainability of a company is gradually becoming a competitive advantage. Sustainable scheduling represents a short-term potential for companies. Concurrently, due to globalization, sustainable scheduling in production networks is attracting significant research interest. The complexity of these optimization problems is high because of a large number of influencing factors, e.g., the geographical location of the customers, the number and the heterogeneity of the factories. As a result, causal relationships often overlap and cannot be separated. In this article, effects are ascertained separately with the help of single-factor experiments in an extensive computational experiment for a distributed permutation flow shop scheduling problem by using a lexicographic mixed-integer-linearprogramming model and fast construction heuristics. Thereby, reasoning about the cause-effect relationships is enabled, promoting the integration of problem-specific knowledge for an efficient design of metaheuristics. Furthermore, valuable insights for management and research result from the derivation of implications.

1 1.1

Introduction Motivation

Global climate change and its effects are among the greatest challenges of our time. In the period from 2010 to 2019 economic losses caused by weather-, climate-, and water-related hazards increased by about 46.6 % from $942 billion to $1381 billion compared to the years 2000 – 2009 [31]. Despite knowledge of the negative impacts, carbon dioxide emissions from energy combustion and industrial processes reached a new record high of 36.3 GtCO2 in 2021 [15] after a significant decrease due to the Covid pandemic. Increasing the ecological and c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  U. Buscher et al. (Eds.): LM 2023, LNLO, pp. 180–208, 2023. https://doi.org/10.1007/978-3-031-38145-4_11

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social sustainability of manufacturing companies is thus becoming continuously more important in order to ensure customer satisfaction and competitiveness. Meanwhile, the advance of globalization has resulted in the international relocation or distribution of factories with the aim of reducing market entry barriers, increasing agility and customer proximity, and diversifying the risk of disruption. As a result, global production networks with complex supplier and customer structures are increasingly common. Sustainable reformation of corporate structure and supplier relationships as well as developing products with resource-conserving production processes and a high level of environmental compatibility, are challenging and hence represent medium to long-term objectives. By aiming for improvements in the short term, production planning and control processes, especially scheduling, is increasingly receiving attention. 1.2

State-of-the-Art

While researchers and practitioners have been dealing with basic scheduling approaches since the second industrial revolution, the research areas of sustainable scheduling and scheduling in production networks are comparatively young. The initial investigation - subject to an ecological objective - was proposed by [20] for a single machine scheduling problem with the target of minimizing the energy consumption. Different energy-saving strategies such as batching of jobs and turning-off machines during idle-times were examined and proven to have a significant impact on the total energy consumption. Concerning a flow shop scheduling problem [13] first investigated a multi-objective problem aiming at the minimization of makespan, peak total power consumption and the carbon footprint. The carbon footprint is assessed by including the CO2 emission factor for electricity consumption. This approach became common practice when minimizing carbon emissions in scheduling problems [8,9,27,33]. Concerning scheduling problems in production networks - commonly referred to as distributed scheduling problems - the traditional scope of decisions consisting of job sequencing and a selection of speed factors is extended by requiring a factory allocation. The distributed permutation flowshop scheduling problem (DPFSP) as analyzed in this article was initially proposed by [21]. Regarding a DPFSP ecological objectives were introduced with the investigation of [8]. The authors studied a carbon-efficient scheduling problem assuming a factoryindependent emission factor. Since these initial investigations, amplified by climate crisis, sustainable scheduling problems have received increasing attention and importance in research [1]. [16] propose a modification of the famous MOEA/D algorithm of [32] to solve a carbon-efficient DPFSP. More recently, an energy-efficient DPFSP with no-idle constraint was investigated by [5]. Lastly, [27] analyze a carbon-efficient DPFSP integrating transport from factories to customers and factory-dependent emission factors for electricity generation.

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Opportunities and Contribution

Although sustainable distributed scheduling problems have been targeted more often lately, analyzing the State-of-the-Art reveals further opportunities for improvement. To begin with, when sustainable optimization criteria are applied in scheduling problems, energy consumption is mainly chosen as the minimization target [23]. However, the amount of emissions produced by electricity generation is strongly linked to the composition of the electricity mix. Thus, the consumption of a kilowatt-hour of electricity produces a widely varying amount of emissions depending on the local carbon emission intensity of electricity generation. In European Union countries, the emission intensity of electricity generation in 2021 spans from 9 gCO2 e/kWh in Sweden to 946 gCO2 e/kWh in Estonia, showing the high inter-country variability [11]. Applied to distributed scheduling problems with multi-national factories, it is not directly the level of energy consumption but the emission intensity that is decisive for the environmental impact resulting from electricity consumption. Likewise, given the high standard deviation of 226.09 gCO2 e/kWh, the assumption of factory-independent emission-factors, as currently practiced in research (e.g., [8,16]), is not purposeful. While customer locations are decision-irrelevant in a scheduling problem within a single factory, the ecological advantageousness of a schedule in production networks is impacted by the customers’ location. As the distance to the customer rises the positive effects of production in the minimum-emission factory are offset by increasing transport emissions. Thus, when surpassing a critical distance, processing a job in a factory with higher production and lower transport emissions becomes more advantageous, implying a risk of determining local instead of global optima. This causal relationship has been analyzed up to the present time, only by [27]. However, a methodology analysis of [27] reveals that effects overlap due to simultaneous changes in customer locations, emission factors, and efficiency of machines. Consequently, deriving the impact of the individual changes is unfeasible, neglecting a thorough comprehension of cause-effect relationships. An understanding of the interrelationships and their significance is a fundamental prerequisite for designing efficient solution approaches. However, the superposition of effects in sophisticated systems impedes such an analysis. The aim of this paper is thus to analyse potential influencing factors and their significance for determining carbon-efficient production schedules. Consequently, the contribution of this paper can be summarized as follows: (1) An extensive experiment assesses the effects of considering heterogeneous factories, factory-dependent emission factors and decision-relevance of transport emissions for a distributed permutation flow shop scheduling problem (DPFSP) with adjustable processing speed. (2) Well-founded managerial implications and recommendations for future research are derived, by combining insights from the cause-effect analysis and present critique of oversimplification in production scheduling.

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The research objectives are pursued via the methodological approach of single-factor experiments. A single-factor experiment is an approach popular in industrial practice, for example embedded in a Plan-Do-Check-Act-cycle for continuous improvement, where at one point in time a single variable is altered and the effect on the system is observed [25]. In complex systems with many inter-dependencies, this approach is often the only practicable way to understand interactions and to initiate measures in a targeted and effective manner. In research, commonly different levels of an independent variable are defined and tested, whereas their effect on a performance measure is assessed and statistically analyzed. Since the analysis is carried out for different parameters and constraints sequentially, this approach resembles an extensive sensitivity analysis. Thereby, the effect on the performance measure can be explicitly attributed to the change in a single influencing factor. In this article, the experiment integrates three dichotomous impact factors: the homogeneity of factories, the homogeneity of emission factors, and the decision relevance of transport emissions. From the full factorial design of the experiment an analysis of compensating or amplifying effects on the objective values is promised. A considerable advantage of the exploratory approach is that the gathered, problem-specific knowledge can subsequently be integrated into the purposeful design of more complex solution approaches. To this end, the article is organized as follows. In Sect. 2, an introduction to the optimization problem is given, focusing on the problem definition, challenges in distributed carbon-efficient scheduling and the assessment of carbon emissions. Section 3 provides a mixed integer linear programming (MILP) formulation and greedy construction heuristics to assess and analyze the cause-effect relationships in Sect. 4. The results are used in Sect. 5 to derive implications for management and research as well as further research needs. Finally, Sect. 6 concludes the investigation and emphasizes its limitations.

2

Introduction to the Optimization Problem

To mathematically define the problem under study, the indices, parameters, and decision variables are introduced following the notation of [27,30] (Table 1). 2.1

Problem Definition

In a permutation flow shop scheduling problem a set of n jobs, which are assumed to be unrelated, is scheduled on a set of m independent machines in a serial arrangement. Each of the m machines gradually processes each job. Moreover, each machine is suitable for processing a job at ϑ different speed levels. Processing a job at a higher speed level reduces the processing time while increasing the energy consumption of that machine. In practice, this relationship is often nonlinear, implying that an increase in the speed level leads to a disproportionate increase in energy consumption.

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M. Sch¨ onheit Table 1. Notation overview following [27]

Index sets

j

Index of jobs j ∈ J = {1, 2, ..., n}

i

Index of machines i ∈ M = {1, 2, ..., m}

f

Index of factories f ∈ F = {1, 2, ..., φ }

k

Index of position of a job k ∈ J = {1, 2, ..., n}

v

Index of speed v ∈ V = {1, 2, ..., ϑ }

Parameters

O j, i, f

Operation of job j on machine i in factory f

P0 j, i, f

Baseline processing time of operation O j, i, f

v P j, i, f

Adjusted processing time of operation O j, i, f at speed v

Sv

Value of the speed factor denoted by speed v

PC 0j, i, f Baseline power consumption of operation O j, i, f at lowest speed (kW) v EC j, Energy consumption of operation O j, i, f at speed v (kWh) i, f f

Carbon emission factor of factory f (kg CO2 e/kWh)

 j, f

Carbon emission to transport job j from factory f to the customer (kgCO2 e)

Decision variables

x vj, k, i, f

Binary variable, 1 if job j occupies position k on machine i at speed v in factory f , 0 otherwise

Ck, i, f

Continuous variable for the completion time of job in position k on machine i in factory f

C ma x

Continuous variable to determine the maximum completion time of all jobs

T CE

Continuous variable to determine the total amount of carbon dioxide emissions

Consequently, the decision set includes the sequencing as well as the determination of the processing speed of the jobs on the individual machines. Once the sequence is determined for the first machine, it is maintained throughout the manufacturing process. In a distributed permutation flow shop scheduling problem (DPFSP) the decision set of the original problem is extended by allocating a job to a factory f . Thus each job is to be processed in one out of φ independent heterogeneous factories. Hence P vj,i, f - the processing time of a job j at speed factor Sv - is determined by dividing the baseline processing time of job j on machine i in factory f denoted as P0j,i, f - by the speed factor Sv . If factories are expected to be homoP0

geneous, the processing times simplify to P vj,i = Sj,v i . The processing of a job on v , which is in size depending on a machine causes an energy consumption EC j,i, f 0 the baseline power of the machine for processing a job PC j,i, f , the selected speed v factor Sv and the processing time P j,i, f at speed Sv . In research, different energy consumption functions are investigated in this regard [28,30]. The energy consumption function adopted in this article is detailed in Eq. 15 in Sect. 4.1. The total energy consumption is specified by the sum of the energy consumption for processing each job on each machine. Accordingly, the production emissions are calculated by multiplying the factory-specific total energy consumption with the factory-specific emission factor  f for electricity generation. If emission factors are assumed to be homogeneous, a factory-independent emission factor  is applied instead. If additionally transportation is considered, the production emissions must be further extended by the carbon emissions for transporting each job from the

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factory to the job-specific customer location. The amount of transport emissions depends on the mode of transport and the product weight and is linearly dependent on the transport distance. Since the problem at hand already deals with a considerable amount of complexity the following simplifying assumptions are made. Processing times are deterministic and non-negative. Setup times are sequence-independent and included in the processing times. Once assigned to a factory, transfer between factories is precluded. After starting the processing on a machine, interruption is neglected. Moreover, machines are available at the start of production and are considered to be continuously available, while processing at most one job at a time. 2.2

Challenges in Distributed Carbon-Efficient Scheduling

The challenges in Green Distributed Scheduling arise mainly from the interaction of the influencing factors and the overlapping of effects. Accordingly, the global optimum concerning carbon emissions is affected by the homogeneity of the factories, the emission intensity of electricity generation in the factories, and the customers’ location. In the simplest case, assuming homogeneous factories (i.e., uniform energy consumption and processing times) and equal distances from the factories to a customer, a job is always allocated to the factory with the lowest emission factor. Under these assumptions, the job-associated carbon emissions are factoryindependent. If factories are now assumed heterogeneous, the allocation decision becomes more complicated, as lower emission factors in one factory may be offset by more efficient machines in another. Thus, job-associated carbon emissions become factory-specific. If differences in energy-saving potentials and processing times between factories are structural or emission intensities of power generation vary greatly, a substantial imbalance in factory utilization may occur. Such effects can result, for example, from factories of varying modernity or investments in the generation and use of green electricity. Additionally, if distances to the customer vary from factory to factory, heterogeneous carbon emissions are associated with the delivery of a job, causing the risk that higher transport emissions negate savings from lower production emissions. Lastly, a risk arises by mistakenly assuming factory-independent emission intensities. Due to the aforementioned high variation in emission intensity, assuming identical or average emission factors may result in a structural over- or underestimation of carbon emissions. As a consequence, assuming homogeneous emission intensities risks misrepresenting reality, which endangers the internal validity of solution approaches because optimal allocation and sequencing in model space do not map industrial reality. The dependencies and interactions drive the complexity of the optimization problem. Nevertheless, knowledge of the dependencies enables an integration of problem-specific knowledge to adapt heuristic approaches according to different properties of the initial problem. The solution approaches of this article are detailed in Sect. 3. However, an explanation of production and transport emissions assessment is required first.

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2.3

Determination of Carbon Emissions

In order to ensure a uniform approach, the determination of carbon emissions from production and transport is based on [27]. Calculation of Transport Emissions Calculating emissions for transporting a job to a customer is a complex undertaking because the amount of carbon emissions emitted depends on a set of highly variable parameters such as the mode of transport, the emission-factor for fuel combustion, the fuel consumption of the individual vehicle, the product weight, the payload, the proportion of empty runs and the distance to the customer. An overview concerning the necessary parameters is given in Table 2. To simplify and standardize the process of carbon emission assessment, the European Committee for Standardization (CEN) developed DIN EN 16258. Simplified, it proposes two different system boundaries for the transport of goods, the Tank-to-Wheel (TtW) and the Well-to-Wheel (WtW) approach. Since the latter systematically accounts for the direct emissions from vehicle operation and further integrates indirect emissions, emitted from fuel supply and energy losses during extraction and production, this investigation is based on the WtW approach. The formula to calculate the carbon emissions caused by the transport of job j to factory f is given by Eq. (1). j f =

mp · gW tW · C · s f j · (1 + per ) m pl

(1)

Table 2. List of symbols for emission calculation. Parameter

Description

Symbol

Unit

Product weight

Weight of the job-associated product

mp

kg

Maximum payload

Maximum transport weight per transport vehicle

m pl

kg

Energy source

Fuel of the transport vehicle (e.g. Diesel, LPG, CNG)

-

-

System boundary

Scope of the assessment of the emission value per unit of power

gW t W , gT t W

k gC O2 e l

Fuel consumption

Amount of fuel consumed per unit of distance

C

l 100 k m

Transportation distance

Distance from factory f to a customer j

sf j

km

per

-

Proportion of empty runs Proportion of the transport distance without freight (i.e., trip to the factory and the return from the customer) in the total transport distance

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Calculation of Production Emissions By consuming energy during non-idle states of a machine (i.e., during processing of a job) carbon emissions in size depending on the power usage of the machine, the processing time and the emission factor for electricity consumption of a factory are emitted. Thus the total amount of production emission is defined by the sum of the energy consumption of each operation in each factory multiplied by the factory-dependent emission factor  f . This relation is depicted by Eq. (2). CE pr oduction =

F  m  n  n  ϑ   f =1 i=1 j=1 k=1 v=1

v v EC j,i, f · x j,k,i, f ·  f

 (2)

Consequently, total carbon emissions associated with a schedule are calculated as the sum of production and transport emissions.

3

Introduction of Solution Approaches

Two solution approaches are proposed in this paper to investigate the causeeffect relationships. A position-based MILP model is proposed to solve scheduling problems with comparatively low complexity. Due to the NP-hard nature of the problem, a heuristic solution approach is required for more complex problems. By focusing on the explorative investigation of the problem instead of determining new benchmark solutions, it is sufficient to apply a fast and greedy constructive heuristic. Due to the ease of implementation and integration of problem-specific knowledge as well as the high performance for permutation flow shop scheduling problems with makespan objective, the Nawaz-Enscore-Ham heuristic is applied in this regard. The solution approaches are detailed below. 3.1

Mixed Integer Linear Programming Model

As a basis for the investigation, the position-based mathematical model according to [27] is used and successively altered to adapt to different problem specifications. Hereby, the binary variable x vj,k,i, f is used to reflect the decisions of allocating a job to a factory, sequencing the jobs in a factory and selecting the processing speed of a job on a machine. As aforementioned, a bi-objective optimization problem to minimize makespan (Cmax ) and carbon emissions (TCE) is investigated, but focusing on finding the lexicographic solutions instead of the Pareto front due to the exploratory aim of the investigation. When applying the lexicographic approach an hierarchical order of the optimization criteria is defined and single-objective optimization problems are solved starting from the highest order objective. The optimal objective function value is subsequently introduced as a constraint to preclude a deterioration of higher-order objective values. Since the lexicographic solutions are to be determined in the scope of this article, each optimization criterion is successively treated with the highest priority.

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The objective functions are detailed in Eq. (3) and Eq. (4) respectively: min f1 = Cmax min f2 = TCE =

F  n  n  ϑ 



f =1 j=1 k=1 v=1

x vj,k, 1, f ·  j f +

m 



v v EC j,i, f · x j,k,i, f ·  f





(3) (4)

i=1

The solution space is bounded by the following constraints: φ  n  ϑ  k=1 f =1 v=1 n  ϑ  j=1 v=1 n  ϑ  j=1 v=1 ϑ  v=1

x vj,k,i, f = 1

∀ j, i

(5)

x vj,k,i, f ≤ 1

∀ f , k, i

(6)

∀ f , i, k > 1

(7)

∀ j, k, i > 1, f

(8)

x vj,k,i, f · P vj,i, f

∀i, f , k > 1

(9)

x vj,k,i, f · P vj,i, f

∀i > 1, k, f

(10)

∀f

(11)

∀k, f ∀ j, k, i, f , v

(12) (13)

∀k, i, f

(14)

x vj,k,i, f ≤ x vj,k,i, f ≥

n  ϑ  j=1 v=1 ϑ  v=1

x vj,k−1,i, f

x vj,k,i−1, f

Ck,i, f ≥ Ck−1,i, f +

n  ϑ  j=1 v=1

Ck,i, f ≥ Ck,i−1, f +

n  ϑ  j=1 v=1

C1, 1, f =

n  ϑ  j=1 v=1

Cmax ≥ Ck,m, f x vj,k,i, f ∈ {0, 1} Ck,i, f ≥ 0

x vj, 1, 1, f · P vj, 1, f

Hereby the constraint set (5) ensures that each operation of each job is performed exactly once. In this regard the constraint set (6) further specifies that in each factory each position on a machine is occupied at most one time. The order of job assignment is determined with the help of constraint set (7). Allocation of the operations of a job to more than one factory is precluded by constraint set (8). The constraint set (9) further ensures, that the completion of the predecessor job operation is required before processing the operation of another job on that machine (i.e., the machine is idle). Correspondingly, constraint set (10) specifies that the operation of a job on the previous machine (i − 1) must be completed before the operation on machine i can be performed. Through a fixation of the machine and position index, constraint set (11) sets the completion time in each factory after performing the first operation on the first machine. Constraint set

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(12) further defines the lower bound for the makespan value. Lastly, by constraint sets (13) and (14) decision variables are introduced and bounded. 3.2

The Nawaz-Enscore-Ham Heuristic

The Nawaz-Enscore-Ham (NEH) heuristic is a fast and greedy constructive heuristic developed to determine high-quality sequences for permutation flow shop scheduling problems with makespan objective [22]. The basic idea is prioritizing jobs with high total processing times over lower total processing times. The jobs are scheduled in the order of descending total processing times, with a job fixed in the position that results in the minimum makespan value. Following to this procedure, a valid solution is constructed after n iterations. This heuristic proved to be highly performant in computational experiments [17], justifying its application in this paper. Before applying the heuristic, however, adaptations for the application to production networks and heterogeneous factories are required. N E H Heuristic f or Makespan Minimization in Production Networ ks

In production networks, the makespan is defined by the factory of the highest partial makespan. For homogeneous factories [21] proposed two allocation mechanisms to adapt the Nawas-Enscore-Ham-Heuristic to distributed scheduling problems. A significantly higher solution quality was obtained when a job was inserted into each position of each factory’s partial sequence and allocated to the factory and position that leads to the current minimum makespan. This modification, subsequently referred to as DNEH heuristic, became State-of-theArt and is therefore applied for homogeneous factories. Applying this heuristic to heterogeneous factories would cause problems since the total processing time of a job varies within factories, which is why two different allocation mechanisms are tested. According to the first mechanism - referred to as the Shortest Total Processing Time (STPT) Rule - a job is assigned to the factory resulting the job’s lowest total processing time. Thereby, the scheduling problem is transformed into f traditional permutation flow shop problems, enabling the application of the basic NEH heuristic within each factory. The associated risk in applying this mechanism is that structural inequalities between factories exist, e.g., due to varying machine age between factories, which could manifest in a specific factory, causing the majority of jobs to be allocated to a single factory. The unilateral allocation would then inevitably lead to sub-optimal makespan values. The second allocation mechanism differs from this approach in that the order in which jobs are scheduled is addressed. Since there are f different total processing times for a job j, the decision regarding the sorting is made based on the mean total processing times. Since this enables sorting according to a single total processing time value, the allocation and sequencing decision is done analogously to the original DNEH heuristic. This modification will henceforth be referred to as the AVG-Rule.

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N E H Heuristic f or Car bon Emission Minimization in Production Networ ks

Integrating problem-specific knowledge is viable to determine the lexicographic solution regarding carbon emissions. Determining the optimal objective value is an easy optimization task since for CO2 optimality, all operations must be performed at minimum processing speed. Consequently, alternative speed factors can be neglected. Based on this dependency, the emission-minimal job allocation can be determined by calculating the job-specific emissions for each factory. The job-specific emissions are composed of the emissions for manufacturing the job in factory f and the subsequent delivery to customer location j. Thus, the allocation is made to the factory, resulting in the job-specific minimum sum of production and transport emissions. As setup and idle energy consumptions are neglected, the partial sequence within a factory does not impact the amount of production emissions. Based on the emission-minimal assignment of jobs to the factories, the traditional NEH heuristic is applied within f to improve makespan without offsetting the carbon emissions objective value. Depending on the case (e.g., homogenous/heterogeneous factories, homogenous/heterogenous emission factors, decision relevance of transport emission), individual components in the objective function are modified or neglected during the job allocation decision.

4

Experimental Evaluation

The purpose of this chapter can be summarized in two main goals: 1. Evaluating the quality of the constructive heuristics as well as the allocation rules for makespan optimization and 2. Determining and analyzing the cause-effect relationships. For this purpose, remarks on generating problem instances and the experimental setup are given first. Subsequently, a performance analysis of the constructive heuristics and allocation rules is conducted in Sect. 4.2. Finally, exploring and evaluating the cause-effect relationships is targeted in Sect. 4.3. The focus is initially on assessing the impact of different problem characteristics on carbon emissions. Further, a sensitivity analysis regarding the product weight is conducted to increase robustness and consolidate cause-effect relationships concerning the decision relevance of transports. Finally, the lexicographic solutions are compared to reveal the consequences of an isolated consideration of the objectives. 4.1

Problem Instances and Experimental Settings

Due to the lack of availability of industrial data, it is necessary to generate synthetic problem instances, whereby the selection of parameters is oriented towards common research practice. Since the DPFSP is NP-hard [21], a distinction is made between a set of small and large problem instances to solve the set of small problem instances to optimality and conclude the solution quality of the heuristics. Due to the expensive computational effort for solving complex scheduling problems the set of large problem instances is exclusively solved

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Table 3. Definition of problem instances Parameter

small

n

8, 10, 12, 14, 16, 18 20, 50, 100, 200, 500

large

m

2, 3, 4, 5

5, 10, 20

φ

2, 3, 4

2, 3, 4, 5, 6, 7

ϑ

3, 5

3, 5 U(100, 200)

Baseline processing times [min]

[1, 1.5, 2], [1, 1.25, 1.5, 1.75, 2]

Machine speed factors

[2, 3, 4, 5]

Baseline power consumption [kW] Emission factors

uniform distribution of 28 european emission factors [11]

Number of Instances

720

720

by the modifications of the NEH heuristic. An overview regarding the selected parameters and distributions is given in Table 3. The parameter combinations of the number of factories, number of jobs and number of machines are based on [21,27]. Since the energy consumption in practice is often not subject to a linear dependency, a quadratic equation depending on the selected speed factor comparable to [30] is chosen in this regard. v 0 v 2 EC j,i, f = PC j,i, f · P j,i, f · Sv

(15)

Consequently increasing the processing speed by selecting a higher speed factor leads to an quadratic increase in the energy consumption. The calculation of a distance-based emission factor requires the availability of viable values for the parameters: payload, transport mode, type of fuel, the proportion of empty runs, fuel consumption, product weight, and the emissions per unit of fuel consumed. As far as possible, the parameterization is based on past values from the industry. Alternatively, reference values from the literature are used. The following Table 4 provides an overview. Concerning the type of transport, freight road transportation is selected since it claimed 74.4 % of the total on-land transport performance in Europe in 2020 [12]. Moreover, Diesel was chosen as the fuel type because diesel-powered trucks by 93,93% still represent the dominant share of trucks [18]. In order to calculate the job-specific transport Table 4. Parameterization of the transport-associated data Parameter

Value

Reference

Fuel consumption (Diesel)

31.4 l/100 km

[18, 27]

Truck payload/weight

25/40 t

Product weight

15 kg

WtW-emissions diesel

3.24 kgCO2 e/l DIN EN 16258

Cross-border proportion of freight runs 85.5 %

[2]

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emissions, the assumption is made that combined delivery occurs at the truck’s maximum payload. To select a suitable product weight is rather difficult since product weight is company- or even job-dependent. Considering that the product weight affects transportation emissions, it is expected that jobs are reallocated as product weight increases. Hence, the product weight is initially set to 15 kg and subsequently tested at different factor levels during a sensitivity analysis Sect. 4.3.2. Concerning the locations of factories and locations a rather simple approach analogously to [27] is chosen. It is assumed that factories and customers are represented as a node randomly located in a rectangular map. Since the European Union was chosen as the article’s reference base, the rectangle’s edge lengths correspond to Europe’s north-south and east-west extent. Thus, each factory and customer is represented by unique location coordinates, enabling a calculation of transportation distances using the euclidean metric. The cases are formed according to the distinguishing characteristics of decision relevance of transports, homogeneity of factories, and homogeneity of emission factors. Since each characteristic is dichotomous, 23 = 8 combinations of the factors emerge, which are given in Table 5 and analyzed in the experiment. An α| β|γ triplet is introduced as an index to uniquely designate the cases. Each field can have the binary values 0 and 1 and thus provides information about the characteristics of a case. The alpha field contains information on the homogeneity of the factories, the beta field on the homogeneity of the emission factors and the gamma field on the decision relevance of transport emissions. A labeling of C111 therefore represents the case with homogeneous factories, homogeneous emission factors and decision relevance of transport emissions. Table 5. Classification of the cases according to the criteria homogeneity of the factories, homogeneity of emission factors and the decision relevance of transports Cases

C000 C001 C010 C011 C100 C101 C110 C111

Homgeneity of factories

no

no

no

no

yes

yes

yes

yes

Homogeneity of emission-factors

no

no

yes

yes

no

no

yes

yes

yes

no

yes

no

yes

no

yes

Decision-relevance of transport emissions no

In order to compare the results for the individual cases, a standardization of the objective function values is necessary. Since the optimization criteria are analyzed separately analogously to [21], the relative percentage deviation (RPD) from the optimal solution according to Eq. (16) is used. RPD =

Solcase − Optsol Optsol

(16)

Here Solcase is the best objective function value determined by the heuristic or exact method for a case, and Optsol is the best objective function value determined across all cases. The statistical analysis is based on an analysis of variance (ANOVA). Therefore, the requirements of normal distribution and variance

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homogeneity must be met. However, since the ANOVA is comparatively robust against violations of the normal distribution [26], Welch’s ANOVA is applied in the absence of variance homogeneity. Additionally, the Kruskal-Wallis with a Dunn-Bonferroni post-hoc test is applied as a non-parametric test to increase robustness and to assess pairwise differences. Concluding an effect size is made by using the correlation coefficient r = √|Z | N and its classification into small (0.1 ≤ r < 0.3), medium (0.3 ≤ r < 0.5), and large effect sizes (0.5 ≤ r) according to [6]. Thereby, Z is the standard test statistics, and N is the sample size. To assess the optimal objective values, the positionbased MILP is implemented in Gurobi and solved with the Gurobi Optimizer in version 9.5.2. A time limit (1200 s) is set for the MILP to limit the computational effort for determining the lexicographic solutions. The modifications of the NEH heuristic were programmed in Python 3.8.8 in Visual Studio Code and are run on a AMD Ryzen Threadripper 1950X 3.4 GHz CPU with 128 GB RAM. 4.2 4.2.1

Performance Analysis of Solution Approaches Performance Analysis for Carbon Emission Minimization

Excellent results could be achieved by exploiting the problem-specific knowledge in the NEH heuristic regarding the emission optimal solution. For all eight cases, the emission optimal solutions of the MILP model were determined. In this regard, it should be noted that C110 - assuming homogeneous factories and homogeneous emission factors without decision relevance of transports - resembles a makespan optimization at the lowest speed level, which is why the DNEH heuristic with makespan objective was applied instead. Since this case neglects transportation emissions and optimizes only for production emissions, these are used for a fair comparison. However, since the jobs are identical in processing times and carbon emissions for this case, the second order optimization with Gurobi concerning the makespan objective does result in a systematic reallocation of - from an emission perspective - factory-indifferent jobs. Consequently, the reallocation from one factory to another to minimize makespan introduces a deviation concerning transport emissions, which is not replicated by the DNEH heuristic. Thus, differences in global emissions between the MILP and the DNEH heuristic result for case C110 . The mean gap between the MILP and DNEH heuristic is 3.07 % for the emission values and 3.09 % for the makespan values. The mean gap between the NEH heuristic and the MILP for all other cases concerning the makespan criterion varies only between 0.23 % and 0.32 %. Further, for each case the differences in the makespan values between the heuristics and the MILP prove to be statistically insignificant in a statistical analysis via a parametric Bonferroni test and the non-parametric Dunn-Bonferroni test (adj. p-value = 1), which again confirms the high solution quality of the NEH heuristic.

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4.2.2 Performance Analysis of Allocation Mechanisms to Minimize Makespan The evaluation of the allocation mechanisms is performed separately for small and large instances, whereby the MILP’s solutions for homogeneous and heterogeneous factories are used as a reference for comparison concerning the set of small instances. The results for the RPD-values are depicted in Fig. 1a and 1b. Since Levene test and Shapiro-Wilk test show that neither variance homogeneity nor normal distribution is fulfilled, Welch’s ANOVA is applied accompanied by the Kruskal-Wallis test. Both tests show significant differences (p < 0.001) between the cases for a significance level of α = 0.01. Moreover, pairwise comparisons using the Dunn-Bonferroni post-hoc test indicate significant differences between each combination of allocation rules and the MILP results (see Table 6).

Fig. 1. Boxplots for deviations in makespan for different allocation rules Table 6. Pairwise Comparisons to Analyze the Allocation Mechanisms Small Instances Z

adj. p

r

Large Instances

effect size

Z

adj. p

r

effect size

medium

-

-

-

-

MILP (Hom. Factories) - DNEH

−9.57