Liner Shipping Network Design Decision Support and Optimization Methods for Competitive Networks
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Dissertation Liner Shipping Network Design Decision Support and Optimization Methods for Competitive Networks M.Sc. Stefan Guericke Schriftliche Arbeit zur Erlangung des akademischen Grades doctor rerum politicarum (dr. rer. pol.) im Fach Wirtschaftsinformatik eingereicht an der Fakultät für Wirtschaftswissenschaften der Universität Paderborn Paderborn, Dezember 2014 Gutachter: 1. Prof. Dr. Leena Suhl 2. Prof. Dr. Wilhelm Dangelmaier The future has many names: For the weak, it means the unattainable. For the fearful, it means the unknown. For the courageous, it means opportunity. Victor-Marie Hugo Acknowledgements This thesis is the result of the research I conducted as a member of the International Graduate School of Dynamic Intelligent Systems and the Operations Research & Decision Support Lab at the University of Paderborn. I would like to thank everybody that supported me to complete this dissertation within three years. Foremost, I would like to thank Prof. Dr. Leena Suhl for sparking my interest on Operations Research during my studies at the University of Paderborn. Furthermore, for providing me the opportunity to work in an enjoyable and productive atmosphere at her chair. I would like to thank my supervisors Prof. Dr. Wilhelm Dangelmaier and Prof. Dr. Gregor Engels for the guidance and the ensuring of a descent research quality. The colleagues at the DS&OR working group provided a challenging and moti- vating research atmosphere. In particular, I want to thank Jun.-Prof. Dr. Kevin Tierney for his critical feedback on the work, Christoph Weskamp for always having time for mutual discussions, and my office colleagues Torben Schramme and Marius Merschformann for the pleasant time. I am deeply thankful for the opportunity of doing the PhD as an ORCONOMY GmbH fellow. I would like to thank the managing directors Dr. Ingmar Steinzen, Dr. Jens-Peter Kempkes and Dr. Stefan Bunte for their guidance and the critical discussions. I would like to thank the whole ORCNOMY team for the support during the last three years, and especially Dr. Stephanie Heller for the feedback on the mathematical proof and sharpening my view on mathematical details. I am indebted to the team of the International Graduate School of Dynamic Intelligent Systems, its head apl. Prof. Dr. Eckhard Steffen, and his assistance Astrid Canisius for the incredible organization, support and the excellent PhD program. I would like to thank anonymous referees for the feedback, discussions and pro- found insight into liner shipping network planning. Credits go as well to all students that participated to the success of this disserta- tion with critical questioning and inspirational ideas. I am deeply indebted to my whole family for sharing their interest in this exciting research through discussions and support. In particular, I want to thank my parents who always believed in me, financially safeguarding me during my studies and always supporting me during my endeavors. Most important, I deeply thank Daniela to share worries, delights and dreams. Stefan Guericke Paderborn, December 2014 ii Contents 1. Introduction 3 1.1. Research Goals . .5 1.2. Outline . .6 2. Liner Shipping Network Planning 9 2.1. Planning Process Overview . .9 2.2. Basic Liner Shipping Network Design Problem . 10 2.3. Route Types and Network Structure . 15 2.4. Transportation of Containerized Cargo . 16 2.4.1. Demand Structure . 16 2.4.2. Container Sizes and Types . 17 2.5. Timing Aspects . 18 2.5.1. Port Calls and their Impact on the Vessels’ Speed . 18 2.5.2. Transit Times in the Maritime Context . 20 2.6. Cooperative Agreements . 22 2.6.1. Liner Conferences, Consortium Agreements and Global Alliances 22 2.6.2. Slot Sharing Agreements . 24 2.7. Liner Service Capacities . 24 2.7.1. Container Vessel Capacities . 25 2.7.2. Port Depth and Vessels’ Deadweight Scale . 25 2.8. Empty Container Repositioning . 27 2.9. Costs and Revenues . 29 2.10. Bunker Cost Uncertainty in the Tactical Planning Horizon . 32 2.11. Summary . 34 3. State-of-the-Art and Research Opportunities 35 3.1. Selected Optimization Techniques . 35 3.1.1. Linear and Mixed Integer Programming . 35 3.1.2. Delayed Column Generation . 37 3.1.3. Metaheuristics . 38 3.1.4. Fitness Approximation in Metaheuristics . 41 3.2. Related Combinatorial Optimization Problems . 43 3.2.1. Vehicle Routing Problems . 43 3.2.2. Pickup and Delivery Problems . 44 3.2.3. Min-Cost Flow Problems . 46 iii 3.3. Liner Shipping Network Planning . 48 3.3.1. Liner Shipping Network Design Problem . 48 3.3.2. Cargo Allocation and Empty Container Repositioning Problems 52 3.3.3. Speed Optimization . 55 3.4. Research Gap and Opportunities . 58 3.5. Goals of this Thesis . 59 4. Evaluating Networks - The Integrated Cargo Allocation Problem 63 4.1. Distinguishing Port Calls in Liner Services . 63 4.2. Common Notation . 65 4.3. Arc-flow Formulation for the Cargo Allocation Problem . 67 4.3.1. Mathematical Model . 68 4.3.2. Bunker Cost Discretization . 71 4.4. Path-Flow Formulation for the Cargo Allocation Problem . 73 4.4.1. Restricted Master Problem . 74 4.4.2. Network Structure for determining Container Paths . 77 4.5. Relaxing the Integrality Constraints for the Bunker Cost . 82 4.6. Numerical Results for the Integrated Cargo Allocation Problem . 87 4.6.1. Problem Instances . 87 4.6.2. Arc-Flow Formulation . 90 4.6.3. Path-Flow Formulation . 93 4.6.4. Comparison of Numerical Results . 95 4.6.5. Choosing an appropriate Approximation Level . 97 4.7. Comparison and Interpretation of Results . 103 5. Improving Networks - The Liner Shipping Network Design Problem 105 5.1. Mixed Integer Formulation . 105 5.1.1. Mathematical Model . 106 5.1.2. Subtour Elimination Constraints . 117 5.1.3. Numerical Results for the Mixed Integer Program . 119 5.2. Metaheuristics . 126 5.2.1. Decomposition Approach for the Metaheuristics . 127 5.2.2. Determine the Fitness of a Solution . 128 5.2.3. Construction Heuristics . 135 5.2.4. Improvement Heuristics . 137 5.2.5. Numerical Results . 143 5.3. Surrogate Extensions to Metaheuristics . 148 5.3.1. Metaheuristic Metacontrol . 148 5.3.2. Numerical Results . 151 5.4. Interpretation of Results . 161 5.5. Bunker Cost Uncertainty in the Tactical Planning Horizon . 162 5.6. Numerical Results from a Global Liner Carrier . 167 iv 6. Integration into a Decision Support System 175 6.1. Decision Support System Components . 175 6.2. Process Overview . 177 6.3. Client-Server Communication . 178 6.4. Graphical User Interface . 181 7. Conclusion 187 7.1. Summary . 187 7.2. Critical Assessment . 188 7.3. Future Research Opportunities . 190 Bibliography 191 List of Figures 211 List of Tables 217 List of Algorithms 219 A. Glossary 221 B. Transformation Algorithm for the Layered Network Structure 223 C. Extended Numerical Results for the Integrated Cargo Allocation Prob- lem 225 C.1. Problem Instances . 225 C.2. Cargo Allocation Approximations . 227 D. Extended Numerical Results for the Liner Shipping Network Design Problem 229 D.1. Liner Shipping Network MIP Formulation . 229 D.2. Parameter Tuning for the Evolutionary Algorithm . 230 D.3. Accuracy of VNS Surrogate Evaluation . 234 E. Calculating Waypoints and Sea Distances 237 1. Introduction According to the World Trade Organization, the worldwide merchandise trade was estimated worth an 18.3 trillion US$ in 20131, which is a worldwide annual increase of 5.3% compared to 2012. The largest proportion of worldwide trade is transported by sea: 80% of the weight and 70% of the value (see UN Conference on Trade and Development (2013) and (Schieck, 2008, p. 177)). Usually, the shipping industry is distinguished into three different operation modes: industrial, tramp and liner (see Christiansen et al. (2004)). In industrial shipping, enterprises own a fleet or have long-term time charter contracts, which basically make them the ship owner. The enterprises are thereby responsible to manage it profitably and ship their cargo to minimal costs. In tramp shipping, the operator owns or charters a fleet and serves available cargo with a basic contracted amount and tries to maximize the profit by working on the spot market (Christiansen et al. (2004)). Tramp shipping usually transports break bulk, such as coal or grain, and liquids, such as liquefied natural gas or crude oil (Stopford, 2009, p. 55). The third mode of operation is liner shipping and is the scope of this thesis. Liner shipping has been an important operation mode that emerged at the end of the 19th century with the introduction of steamships (Stopford, 2009, p. 28, 31). Liner operators steam according to a published regular schedule, similar to a bus line, independent of the utilization of the vessels (Christiansen et al. (2004), Schieck (2008)). From a customer’s (shipper’s) point of view, the advantages are regular transportation opportunities, relatively reliable sailing schedules and predictable transit times (Brooks, 2000, p. 2). Today, liner shipping is mainly connected with the transportation of general cargo in standardized containers, which accounted for 16% of the seaborne trade in tons and 50% of the value in 2013 (see UN Conference on Trade and Development (2013)). The main advantage of the containerization of commodities during the 1950s is the decreased port time due to increased intermodal efficiency (Levinson (2006)). Beside the twenty foot equivalent unit (TEU), many different specialized container types exist that allow the transportation of break bulk and liquids as well. In general, container vessels are not competitive compared to large scale break bulk vessels for high volumes (Schieck, 2008, p. 209) and are rather used to transport small volumes for specific end customers. Other commodities that are transported by liner operations are vehicles (using roll-on/roll-off vessels) or project cargo, such as sailing boats or other bulky commodities (Schieck, 2008, p.