Computational Logistics: 11th International Conference, ICCL 2020, Enschede, The Netherlands, September 28–30, 2020, Proceedings [1st ed.] 9783030597467, 9783030597474

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LNCS 12433

Eduardo Lalla-Ruiz Martijn Mes Stefan Voß (Eds.)

Computational Logistics 11th International Conference, ICCL 2020 Enschede, The Netherlands, September 28–30, 2020 Proceedings

Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA

Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA

12433

More information about this series at http://www.springer.com/series/7407

Eduardo Lalla-Ruiz Martijn Mes Stefan Voß (Eds.) •

•

Computational Logistics 11th International Conference, ICCL 2020 Enschede, The Netherlands, September 28–30, 2020 Proceedings

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Editors Eduardo Lalla-Ruiz University of Twente Enschede, The Netherlands

Martijn Mes University of Twente Enschede, The Netherlands

Stefan Voß University of Hamburg Hamburg, Germany

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-59746-7 ISBN 978-3-030-59747-4 (eBook) https://doi.org/10.1007/978-3-030-59747-4 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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

The increasing complexity of present-day logistics operations as well as the increasing availability of information, makes it imperative to jointly use optimization and artificial intelligence for devising computational data-driven intelligent decision support. Recently, important efforts and initiatives from all sides of optimization and artificial intelligence have been undertaken to improve logistics operations with sophisticated algorithms and information systems. This resulted in advances in both theoretical and practical aspects as well as technical innovations in several logistics sectors, such as maritime shipping, freight transportation, urban distribution, multi-modal transportation, warehousing, and inventory management. This way, the trend towards computational logistics, as the glue between decision making and operations, has become a key component for economic and industrial growth. On the other hand, in the middle of the COVID-19 world crisis, advances in this area are more necessary than ever to support speedy operations, to flexibly adapt supply chains to distribution disruptions, and to avoid potential shortages. Computational Logistics covers the management of logistics’ activities and tasks through the joint use of computational technologies and advanced decision support and optimization techniques. It is applied in several areas, e.g., the flow and storage of goods and services as well as the flow of related information. In this context, modeling and algorithmic approaches are developed, verified, and applied for planning and executing complex logistics tasks, e.g., for finding the most efficient routing plan and schedule to transport passengers or distribute goods. The models and algorithms are integrated with computing technologies, not only for getting satisfactory results in reasonable times, but also exploiting interactivity with the decision maker through visual interfaces, and for extracting knowledge from data to improve future decision making. This promotes the joint effort of practitioners and scholars for better understanding and solving the logistics problems at hand. The International Conference on Computational Logistics (ICCL) is a forum where recent advances in the computational logistics research area are presented and discussed. This volume offers a selection of 49 peer-reviewed papers out of 93 contributions submitted to the 11th ICCL edition, virtually held at the University of Twente, The Netherlands, during September 28–30, 2020. The papers show various directions of importance in computational logistics, classified into five topic areas reflecting the interest of researchers and practitioners in this field. The papers in this volume are grouped according to the following parts: 1. Maritime and Port Logistics Maritime logistics is the backbone of global supply chains and international trade. The performance and functioning of its related activities are remarkably influenced by the quality of its planning and management. In ICCL 2020, the contributions that fall into this area relate to, among others, port development, waterway transport, stowage planning, container management, and various real-world applications.

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2. Vehicle Routing and Scheduling This well-known family of optimization problems constitutes an important part of real-world transport and logistics activities. Due to the many specific real-world features, there is a strong necessity of modeling and developing efficient solution approaches as well as formalizing cases that permit advancements in this area. The papers in this category address, among others, dynamic vehicle routing, collaborative logistics, inventory routing, cross-docking, green and electric vehicle routing, pickup and delivery, customer prioritization, and drivers’ considerations. 3. Freight Distribution and City Logistics The progress in transportation and economic trade as well as the development of cities and regions require the adaptation and update of current systems to cope with changes that also involve sustainability and environmental impact. The works in this part relate to a diverse range of topics, such as vehicle repositioning, carsharing, travel time predictions, smart cities, waste collection, and truck platooning. 4. Network Design and Scheduling Designing and scheduling logistics networks is among the most important tactical and strategic decisions in supply chain management. This area pursues the efficient organization, modeling, and management of the diverse resources and operations involved in such a way that the flow of products, services, or persons is as good as possible. Contributions considering supply chain networks, logistic flow problems, shortest path algorithms, and matching problems fall into this category. 5. Selected Topics in Logistics The papers that appear in this area relate to a range of topics concerning various computational logistics topics such as cash distribution, logistics-related serious games, e-commerce, game theory applications, pricing, order picking and loading problems, and quality investments. The ICCL 2020 was the 11th edition of this conference series, following the earlier ones held in Shanghai, China (2010, 2012), Hamburg, Germany (2011), Copenhagen, Denmark (2013), Valparaiso, Chile (2014), Delft, The Netherlands (2015), Lisbon, Portugal (2016), Southampton, UK (2017), Salerno, Italy (2018), and Barranquilla, Colombia (2019). The editors thank all the authors for their contributions as well as the program committee and reviewers for their invaluable support and feedback. Finally, we would like to express our gratitude to Julia Bachale for her helpful support and assistance during the preparation of the conference. We trust that the present volume supports the continued advances within computational logistics and inspires all participants and readers to its fullest extent. September 2020

Eduardo Lalla-Ruiz Martijn Mes Stefan Voß

Organization

Program Committee Panagiotis Angeloudis Tolga Bektas Francesco Carrabs Carlos Castro Raffaele Cerulli Joachim Daduna Adriana Daza René De Koster Yingjie Fan Elena Fernández Monica Gentili Rosa González Ramírez Hans-Dietrich Haasis Richard Hartl Geir Hasle Wouter van Heeswijk Leonard Heilig Alessandro Hill Jan Hoffmann Manuel Iori Jiangang Jin Raka Jovanovic Herbert Kopfer Eduardo Lalla-Ruiz Jasmine Siu Lee Lam Gilbert Laporte Janny Leung Dirk Mattfeld Frank Meisel Gonzalo Mejía Belen Melián-Batista Martijn Mes José Marcos Moreno-Vega Ioannis Lagoudis Rudy Negenborn Dario Pacino Julia Pahl Carlos Paternina-Arboleda

Imperial College London, UK The University of Liverpool, UK University of Salerno, Italy Universidad Federico de Santa María, Chile University of Salerno, Italy Berlin School of Economics and Law, Germany Universidad del Norte, Colombia Erasmus University Rotterdam, The Netherlands Erasmus University Rotterdam, The Netherlands Universidad de Cádiz, Spain University of Louisville, USA Universidad de Los Andes, Colombia University of Bremen, Germany University of Vienna, Austria SINTEF Digital, Norway University of Twente, The Netherlands University of Hamburg, Germany California Polytechnic State University, USA UNCTAD, Switzerland University of Modena and Reggio Emilia, Italy Shanghai Jiao Tong University, China QEERI, Qatar University of Bremen, Germany University of Twente, The Netherlands Nanyang Technological University, Singapore HEC Montréal, Canada University of Macau, Macau, China TU Braunschweig, Germany University of Kiel, Germany Universidad de La Sabana, Colombia Universidad de La Laguna, Spain University of Twente, The Netherlands Universidad de La Laguna, Spain University of Piraeus, Greece Delft University of Technology, The Netherlands Technical University of Denmark, Denmark University of Southern Denmark, Denmark Universidad del Norte, Colombia

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Organization

Mario Ruthmair Juan José Salazar González Frederik Schulte Marco Schutten Xiaoning Shi Douglas Smith Grazia Speranza Shunji Tanaka Kevin Tierney Thierry Vanelslander Stefan Voß

University of Vienna, Austria Universidad de La Laguna, Spain Delft University of Technology, The Netherlands University of Twente, The Netherlands University of Hamburg, Germany University of Missouri-St. Louis, USA University of Brescia, Italy Kyoto University, Japan Bielefeld University, Germany University of Antwerp, Belgium University of Hamburg, Germany

Additional Reviewers Adina Aldea André Amaral Breno Beirigo Rob Bemthuis Melissa Buballa Giovanni Campuzano Kaimin Chen Jésica De Armas Alan Dávila Roberto Díaz Yun Fan Alejandro Fernández Berry Gerrits Stefan Guericke Nicolás Gálvez Mariam Gómez Hipolito Hernandez Pérez Martijn Koot

Alberto Locatelli Johan Los Javier Maturana-Ross Pedro Nunes Makbule Ozler Nadia Pourmohammadzia Michael Roemer Alex Sangers Dingena Schott Jakov Schulte Peter Schuur Silvia Schwarze Dimitris Souravlias Uilke Stelwagen Robert van Steenbergen Pieter Vansteenwegen Daniel Wetzel Giorgio Zucchi

Contents

Maritime and Port Logistics Evaluating Port Development Strategies for a Modal Shift: A Norwegian Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andreas Breivik Ormevik, Stein Ove Erikstad, and Kjetil Fagerholt Pickup and Delivery Problem with Transshipment for Inland Waterway Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yimeng Zhang, Bilge Atasoy, Dimitris Souravlias, and Rudy R. Negenborn Ferry Service Network Design for Kiel fjord . . . . . . . . . . . . . . . . . . . . . . . Ingvild Eide Aslaksen, Elisabeth Svanberg, Kjetil Fagerholt, Lennart Christian Johnsen, and Frank Meisel

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36

Smart Containers with Bidding Capacity: A Policy Gradient Algorithm for Semi-cooperative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wouter van Heeswijk

52

Analyzing the Impact of the Northern Sea Route on Tramp Ship Routing with Uncertain Cargo Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingyu Li, Kjetil Fagerholt, and Peter Schütz

68

Stowage Planning with Optimal Ballast Water . . . . . . . . . . . . . . . . . . . . . . Beizhen Jia, Kjetil Fagerholt, Line Blander Reinhardt, and Niels Gorm Malý Rytter

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Waterborne Hinterland Transports for Floating Port Terminals . . . . . . . . . . . Gerrit Assbrock, Jens Ley, Ioannis Dafnomilis, Mark B. Duinkerken, and Dingena L. Schott

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An Optimization Model for Defining Storage Strategies for Export Yards in Container Terminals: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniela Ambrosino and Haoqi Xie

119

Vehicle Routing and Scheduling Dynamic Assignment Vehicle Routing Problem with Time Windows . . . . . . Kim J. Los, Frank Phillipson, Elisah A. van Kempen, Hans J. Quak, and Uilke Stelwagen

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Time-Dependent Travel-Time Constrained Inventory Routing Problem . . . . . Faycal A. Touzout, Anne-Laure Ladier, and Khaled Hadj-Hamou Vehicle Routing Problem with Reverse Cross-Docking: An Adaptive Large Neighborhood Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, and Vincent F. Yu

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Solving a Bi-Objective Rich Vehicle Routing Problem with Customer Prioritization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tim van Benthem, Mark Bergman, and Martijn Mes

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A Genetic Algorithm to Minimise the Number of Vehicles in the Electric Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bertran Queck and Hoong Chuin Lau

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Decentralized Combinatorial Auctions for Dynamic and Large-Scale Collaborative Vehicle Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Johan Los, Frederik Schulte, Margaretha Gansterer, Richard F. Hartl, Matthijs T. J. Spaan, and Rudy R. Negenborn Metaheuristic Approaches for the Fleet Size and Mix Vehicle Routing Problem with Time Windows and Step Cost Functions . . . . . . . . . . . . . . . . João L. V. Manguino and Débora P. Ronconi Cyclical Inventory Routing with Unsplittable Pick-Up and Deliveries . . . . . . Jakob Schulte, Michael Römer, and Kevin Tierney The Multistage Stochastic Vehicle Routing Problem with Dynamic Occasional Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jørgen Skålnes, Lars Dahle, Henrik Andersson, Marielle Christiansen, and Lars Magnus Hvattum Cumulative VRP with Time Windows: A Trade-Off Analysis. . . . . . . . . . . . Alejandro Fernández Gil, Mariam Gómez Sánchez, Eduardo Lalla-Ruiz, and Carlos Castro

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231 246

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Freight Distribution and City Logistics Formulations of a Carsharing Pricing and Relocation Problem . . . . . . . . . . . Giovanni Pantuso

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Evolutionary Approach for the Multi-objective Bike Routing Problem . . . . . . Pedro Nunes, Ana Moura, and José Santos

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Quantifying the Effect of Flexibility and Information Sharing in Transportation Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ebba Celius, Madeleine Reehorst, Heidi Dreyer, and Peter Schütz

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Contents

A Bin Packing Problem with Mixing Constraints for Containerizing Items for Logistics Service Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sajini Anand and Stefan Guericke Distance Approximation for Dynamic Waste Collection Planning . . . . . . . . . Fabian Akkerman, Martijn Mes, and Wouter Heijnen

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342 356

Daily Distribution of Duties for Crew Scheduling with Attendance Rates: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Scheffler and Janis Sebastian Neufeld

371

A Heuristic Algorithm for Finding Attractive Fixed-Length Circuits in Street Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rhyd Lewis

384

Minimizing Movements in Location Problems with Mobile Recycling Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eduardo Alarcon-Gerbier and Udo Buscher

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Travel Time Prediction Using Tree-Based Ensembles . . . . . . . . . . . . . . . . . He Huang, Martin Pouls, Anne Meyer, and Markus Pauly Platooning of Automated Ground Vehicles to Connect Port and Hinterland: A Multi-objective Optimization Approach . . . . . . . . . . . . . . . . . . . . . . . . . Nadia Pourmohammad-Zia, Frederik Schulte, Dimitris Souravlias, and Rudy R. Negenborn Dynamic Pricing for User-Based Rebalancing in Free-Floating Vehicle Sharing: A Real-World Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nout Neijmeijer, Frederik Schulte, Kevin Tierney, Henk Polinder, and Rudy R. Negenborn

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428

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Automated and Autonomous Driving in Freight Transport - Opportunities and Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joachim R. Daduna

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Learning-Based Co-planning for Improved Container, Barge and Truck Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rie B. Larsen, Bilge Atasoy, and Rudy R. Negenborn

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Overcoming Mobility Poverty with Shared Autonomous Vehicles: A Learning-Based Optimization Approach for Rotterdam Zuid . . . . . . . . . . . Breno Beirigo, Frederik Schulte, and Rudy R. Negenborn

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Idle Vehicle Repositioning for Dynamic Ride-Sharing . . . . . . . . . . . . . . . . . Martin Pouls, Anne Meyer, and Nitin Ahuja

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Contents

Smart City: A Perspective of Emergency and Resilience at a Community Level in Shanghai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoning Shi, Wenchen Sun, Stefan Voß, and Jiangang Jin

522

Network Design and Scheduling A Shortest Path Algorithm for Graphs Featuring Transfer Costs at Their Vertices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rhyd Lewis A Global Intermodal Shipment Matching Problem Under Travel Time Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenjing Guo, Bilge Atasoy, Wouter Beelaerts van Blokland, and Rudy R. Negenborn Cutting Planes for Solving Logistic Flow Problems . . . . . . . . . . . . . . . . . . . Kishan Kalicharan, Frank Phillipson, and Alex Sangers

539

553

569

Deep Reinforcement Learning and Optimization Approach for Multi-echelon Supply Chain with Uncertain Demands . . . . . . . . . . . . . . Júlio César Alves and Geraldo Robson Mateus

584

The Multi-period Petrol Station Replenishment Problem: Formulation and Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luke Boers, Bilge Atasoy, Gonçalo Correia, and Rudy R. Negenborn

600

Simulation Approach for Container Assignment Under Uncertainty . . . . . . . . Wouter J. de Koning, Frank Phillipson, and Irina Chiscop A Mathematical Model to Route Technicians for Inland Waterway Shipping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Melissa Buballa, Daniel Wetzel, Kay Lenkenhoff, and Kevin Tierney

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Selected Topics in Logistics Reactive GRASP-Based Algorithm for Pallet Building Problem with Visibility and Contiguity Constraints . . . . . . . . . . . . . . . . . . . . . . . . . Manuel Iori, Marco Locatelli, Mayron C. O. Moreira, and Tiago Silveira Game Theoretic Analysis of State Interventions to Reduce Customer Returns in E-Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Beranek Fair User Equilibrium in a Transportation Space-Time Network . . . . . . . . . . Lianne A. M. Bruijns, Frank Phillipson, and Alex Sangers

651

666 682

Contents

Comparison of Manual and Automated Decision-Making with a Logistics Serious Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martijn Mes and Wouter van Heeswijk Pricing and Quality Investments in a Mixed Brown-Green Product Market. . . Arka Mukherjee and Margarida Carvalho

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698 715

Increasing the Practical Applicability of Order Picking Operations by Integrating Classification, Labelling and Packaging Regulations . . . . . . . . Sarah Vanheusden, Teun van Gils, Katrien Ramaekers, and An Caris

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A Solution Approach to The Problem of Nesting Rectangles with Arbitrary Rotations into Containers of Irregular Convex and Non-Convex Shapes. . . . . Alexandre Romanelli and André R. S. Amaral

747

Cash Distribution Model with Safety Constraints . . . . . . . . . . . . . . . . . . . . William J. Guerrero, Angélica Sarmiento-Lepesqueur, and Cristian Martínez-Agaton

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Maritime and Port Logistics

Evaluating Port Development Strategies for a Modal Shift: A Norwegian Case Study Andreas Breivik Ormevik1 , Stein Ove Erikstad1 , and Kjetil Fagerholt2(B) 1

Department of Marine Technology, Norwegian University of Science and Technology, 7491 Trondheim, Norway {andreas.ormevik,stein.ove.erikstad}@ntnu.no 2 Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway [email protected]

Abstract. We study the design of a multi-modal distribution network for the transportation of incoming containers from a container terminal to nearby customer regions. The motivation for the study has been the relocation of the existing cargo terminal in the Port of Bergen, which is expected to increase the road transportation need in the region. To mitigate the consequences of increased driving distances, maritime solutions have been suggested as replacements for truck transportation, but as no such concepts currently exists, more knowledge and insight is needed. Therefore, in this paper we propose a Mixed Integer Programming (MIP) model for optimizing and evaluating strategies for a modal shift in the final stage of the supply chain, i.e. the short distance final distribution from the main terminal to the customer regions. We use it on the case study for the Port of Bergen to analyze whether it is possible to come up with solutions where a significant share of the distribution is done by small electric (and possibly autonomous) container ships instead of trucks. The analyses indicate that a multi-modal distribution network can be a cost-effective option for this particular case.. Keywords: Maritime transportation Programming

1

· Modal shift · Mixed Integer

Introduction

More than 90% of the global trade is performed by ships (IMO 2019). Container shipping, which has more than doubled in the last 15 years, is a substantial part of this. Among the busiest routes for containerized cargo is the route from East Asia to Northern Europe, with the ports in Rotterdam, Antwerp and Hamburg being the three largest. From these ports, several optional transportation modes for further distributions exist, including truck or rail transportation, as well as further distribution by smaller ships to different end customer regions all across Europe. c Springer Nature Switzerland AG 2020  E. Lalla-Ruiz et al. (Eds.): ICCL 2020, LNCS 12433, pp. 3–17, 2020. https://doi.org/10.1007/978-3-030-59747-4_1

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A. B. Ormevik et al.

In Norway, the total volume of distributed cargo is expected to increase by approximately 70% towards 2050, where truck transportation is expected to have the largest share (The Ministry of Transport 2017). Trucks are often the preferred transportation mode due to lower costs and a higher flexibility through more frequent deliveries than ship transportation can offer. Despite being a flexible and cost-efficient way of distributing cargo, an increase in road-based transportation using current technologies might contribute to amplify some of the challenges that hinders a sustainable development. This is seen in several Norwegian (and European) cities, where road transport is a significant source to air pollution and noise. Some ports, such as the Port of Bergen on the west coast of Norway, shown in Fig. 1, is today located near the city center, which generates large amounts of traffic through urban areas. The port also restricts the access to the sea and occupies large land areas suited for urban development (Gulbrandsen et al. 2018). To overcome these challenges, it has been decided to move the cargo terminal of the Port of Bergen from the city center to one of the surrounding regions in 2025 (Bergen Havn 2018). However, this will induce a larger transportation need as the distance to the main customer regions increases. One of the suggested solutions to mitigate the increase in road traffic, has been to strengthen the maritime transportation in the region, also for shorter distances from the main terminal to surrounding end customer regions (Berg and Haram 2018). This will be consistent with both national and international goals and visions for future transportation systems. The European Union has set a goal of shifting 30% of the road transportation to other transportation modes such as rail or maritime transport by year 2030, and a similar benchmark of at least 50% by year 2050 (European Commission 2011). The largest container port in Europe, the port of Rotterdam, has formulated an even more ambitious vision for inland transportation of cargo handled in the port - at most 35% of the further distribution should take place by trucks, 45% transported on inland waterways (barges) and the remaining 20% should be distributed by rail transport (OECD 2010). Also in Norway there is a national interest beyond the port of Bergen of working towards a modal shift in the domestic cargo transportation, with a goal of shifting 30% of the current road transportation to alternative transportation modes within 2030 (The Ministry of Transport 2017). It should be noted that the above mentioned visions mostly apply for larger transportation distances, defined as more than 300 km. Less attention has been given to the final stages of the supply chain over shorter distances, where requirements to delivery frequencies can, in many cases, still be satisfied by ships. However, as the sailing distances get shorter, port fees, cargo handling activities and manning costs contribute to a larger portion of the total ship transportation cost. It has traditionally been hard to reduce the impact of these cost drivers, which will be a necessity to increase the share of maritime transportation. The introduction of new ship concepts, such as autonomous (and unmanned) ships, may facilitate such a change.

Evaluating Port Development Strategies for a Modal Shift

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Fig. 1. Geographical distribution of cargo to different geographic locations in the Bergen region (Sundfjord 2015).

In the Norwegian shipping segment, several new concepts have been developed in recent years aiming to replace significant amounts of the current road-based transportation. In collaboration with the Kongsberg group, the Norwegian fertilizer company Yara has developed the world’s first fully electric and autonomous container ship, Yara Birkeland. The vessel will have a capacity of 120 TEUs (Twenty-foot Equivalent Units), and is estimated to reduce the annual emissions of NOx and CO2 corresponding to 40 000 journeys by trucks between the facilities at Herøya, Brevik and Larvik (Kongsberg 2017). The Norwegian grocery wholesaler ASKO has also developed a similar concept for reducing the need for truck transportation, by using an electric and autonomous ro-ro vessel with a capacity of 16 trucks. The concept will replace two million ton-kilometers per year, and cut the CO2 emissions by 5000 tons (ASKO 2019). The logistics company North Sea Container Line developed a concept for transshipment of containers at sea, using smaller feeder vessels to serve ports along the Norwegian coast and synchronize the routes with the route of the main vessel distributing cargo from the European ports. This concept has been further studied with respect to optimizing its performance (Holm et al. 2019).

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A transportation system including both road-based and maritime transportation, with time window requirements for multiple commodities, is studied in Ayar and Yaman (2012). A MIP model is formulated, aiming to minimize the transportation costs. A similar multi-modal distribution network is studied in Chandra et al. (2020), where the potential for a growth in the modal shift is being analyzed for a coastal shipping case for distribution of cars in India. A MIP model is formulated and used to determine the fleet size and mix as well as the number of voyages performed on a set of feasible routes generated a priori, in order to minimize the overall costs. While a large number of the reviewed articles focus on minimizing cost, there are also some studies considering mitigation of CO2 -emissions. Zhou et al. (2018) point out the lack of multi-objective studies in the existing literature, and present network flow models aiming to optimize a multi-modal transportation network with respect to both total costs and emissions of CO2 equivalents (CO2 e). Five different scenarios of supply chain design in the UK, each with a different level of investments in port infrastructures and expansion projects, are analyzed by Rodrigues et al. (2015). The five scenarios are analysed with respect to both costs and CO2 e emissions, aiming to motivate a modal shift in the UK. The motivation for studying multi-modal transportation networks in this paper has been the relocation of the existing cargo terminal in the Port of Bergen, which is expected to increase the road transportation need in the region. In order to mitigate the consequences of increased driving distances, maritime solutions have been suggested as replacements for truck transportation, but as no such concepts currently exists, more knowledge and insight is needed. Therefore, in this paper we propose a Mixed Integer Programming (MIP) model for optimizing and evaluating strategies for a modal shift in the final stage of the supply chain, i.e. the short distance final distribution from the main terminal to the customers. Furthermore, we use it on the case study for the Port of Bergen to see whether it is possible to come up with solutions where a significant share of the distribution is done by small electric (and possibly autonomous) container ships instead of trucks. In this MIP model, we take as input an estimated cargo demand from the Port of Bergen to the different customer regions surrounding Bergen, as well as cost data for a few alternative small-sized container ships that can potentially be used in this distribution. The model will then, for a given number of different input scenarios, determine the share of truck vs. ship transportation for the final distribution and the optimal fleet of small-sized container ships, and as such provide valuable decision support for analyzing the effects from a modal shift after the relocation of the Port of Bergen. Bergen and its surroundings consists of numerous fjords and islands, which makes the topography especially interesting for this, see Fig. 1. Section 2 provides a problem definition and presents a MIP model for analyzing it. Section 3 presents the computational results from the case study for the Port of Bergen, while concluding remarks are provided in Sect. 4.

Evaluating Port Development Strategies for a Modal Shift

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7

Problem Definition and Mathematical Formulation

In the following we provide a problem definition in Sect. 2.1 together with the mathematical notation, while the MIP model is presented in Sect. 2.2. 2.1

Problem Definition and Notation

The planning problem deals with designing a distribution network for the transportation of containers from a main terminal to a given set of customer regions (as illustrated in Fig. 1). There are two possible alternatives for transportation available: 1) Direct truck transportation from the main terminal to the customer regions, and 2) Multi-modal transportation with ships to unloading port(s) in or close to the customer regions, and then truck transportation from the unloading port(s) to the customers. The problem can be defined as to determine: – – – –

which ships to use, i.e. the optimal fleet size and mix, the deployment of the ships, i.e. the ship routes, which unloading ports to use, and the cargo flow through the network, including mode of transportation.

To define this problem mathematically, we need the following notation. There is a set of customer regions, K, where region k has a given monthly demand from the main terminal given by Dk . We assume there is a set of available ship types, V, to choose from, each with a given capacity, Qv . There is also a set of candidate unloading ports, P, that can be used in the distribution of containers from the main terminal to the customer regions. Furthermore, we define Rv as the set of candidate routes that can be used by ships of type v, while Pr is the set of unloading ports along route r. The parameter Tvr is the time it takes for a vessel of type v to perform route r (including the loading and unloading time, assuming that the ship is fully loaded). We need to define the following cost parameters: CvF represents the fixed costs per vessel of type v, i.e. it represents the time charter rate that is also supposed VS is the variable sailing cost for a vessel of type v to cover the building costs. Cvr VM is the unit cost (per container) for loading the vessels to operate route r. C in the main terminal, while CiV H is the unit handling cost at unloading port i. FT represent the unit cost for direct truck distribution from the main CkDT and Cik terminal and the final truck distribution from unloading port i to customer region k, respectively. In this particular case study, the candidate unloading ports either do not exist or need to be upgraded. It will therefore be a decision regarding which unloading ports to open (i.e. build or upgrade), and we assume that the fixed cost for opening port i is given by CiF . The fixed costs for investing in ships, CvF , and ports, CiF , have been translated into equivalent periodic costs for the planning horizon, T (set to 30 days), based on an expected lifetime (chosen as 20 years for the ships and 40 years for the ports) and a given discount rate (set to 5%). The decision variables are as follows: The integer variable uv represents the number of ships of type v to be used, while yvr is the total number of voyages on

8

A. B. Ormevik et al.

L route r performed by vessels of type v over the planning horizon. qvr represent U is the total quantity transported along route r by all vessels of type v, while qivr the total quantity unloaded in port i by vessels of type v sailing route r. The total quantity transported directly by truck from the main terminal to customer k is given by lkDT , while the total quantity transported by trucks as final distribution FT . Finally, we let the binary from port i to customer region k is given by lik variable δi be equal to 1 if unloading port i is opened, and 0 otherwise.

2.2

Model

By using the notation introduced in the previous section, we can formulate our planning problem with the following MIP model.    CkDT lkDT + CvF uv + CiF δi minimize z = v∈V

k∈K

+

 

L C V M qvr

+

v∈V r∈Rv

+

  

 

i∈P VS Cvr yvr

v∈V r∈Rv U CiV H qivr +

v∈V r∈Rv i∈Pr



(1)

FT FT Cik lik

k∈K i∈P

subject to L − qvr



U qivr = 0,

v ∈ V, r ∈ Rv

(2)

i∈Pr

 

U qivr −

v∈V r∈Rv

lkDT +





FT lik = 0,

i∈P

(3)

k∈K FT lik ≥ Dk ,

k∈K

(4)

i∈P L − Qv yvr ≤ 0, v ∈ V, r ∈ Rv qvr 1  Tvr yvr , v ∈ V uv ≥ T r∈R v    U qivr ≤ Dk δi , i ∈ P v∈V r∈Rv

(5) (6) (7)

k∈K

Non-negativity requirements are imposed for the load variables, i.e. all variU L FT , qvr , lkDT , and lik , while we make sure that the fleet selection and ables qivr deployment variables, uv and yvr take non-negative integer values. Finally, we impose binary requirements on the port selection variables, δi . The objective function (1) minimizes the total cost over the planning horizon, which consists of the following cost components: a) costs for the direct truck distribution from the terminal to the customer regions, b) fixed cost for the selected ships, c) fixed costs for the selected unloading ports, d) variable loading costs at the main terminal, e) variable sailing costs, f) variable handling costs

Evaluating Port Development Strategies for a Modal Shift

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at the unloading ports, and g) the costs for the final truck distribution from the unloading ports to the customer regions. Constraints (2) ensure that the load balance for all vessel types is maintained, i.e. the total cargo quantity loaded on board the vessels of each type that sail a given route must equal the sum of the cargo quantity unloaded to all ports along the given route. Constraints (3) express the cargo flow balance between unloaded and further distributed cargo in each p