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E-grāmata: Optimizing Liner Shipping Fleet Repositioning Plans

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Optimizing Liner Shipping Fleet Repositioning PlansPalielināt
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This monograph addresses several critical problems to the operations of shipping lines and ports, and provides algorithms and mathematical models for use by shipping lines and port authorities for decision support. One of these problems is the repositioning of container ships in a liner shipping network in order to adjust the network to seasonal shifts in demand or changes in the world economy. We provide the first problem description and mathematical model of repositioning and define the liner shipping fleet repositioning problem (LSFRP). The LSFRP is characterized by chains of interacting activities with a multi-commodity flow over paths defined by the activities chosen. We first model the problem without cargo flows with a variety of well-known optimization techniques, as well as using a novel method called linear temporal optimization planning that combines linear programming with partial-order planning in a branch-and-bound framework. We then model the LSFRP with cargo flows, using several different mathematical models as well as two heuristic approaches. We evaluate our techniques on a real-world dataset that includes a scenario from our industrial collaborator. We show that our approaches scale to the size of problems faced by industry, and are also able to improve the profit on the reference scenario by over US$14 million.

1 Introduction
1(6)
1.1 Approach and Contributions
3(2)
1.2 Outline
5(2)
2 Containerized Shipping
7(14)
2.1 Containers
8(1)
2.2 Liner Shipping Networks
9(5)
2.2.1 Services
10(2)
2.2.2 Network Structure
12(2)
2.3 Vessels
14(2)
2.3.1 Fuel Consumption and Slow-Steaming
14(2)
2.3.2 Stowage
16(1)
2.4 Carrier Alliances
16(1)
2.5 Ports and Container Terminals
17(4)
3 Liner Shipping Fleet Repositioning
21(14)
3.1 Repositioning Overview
22(2)
3.2 Phase-Out and Phase-In
24(1)
3.3 Repositioning Activities
25(6)
3.3.1 Sailing and Slow-Steaming
25(1)
3.3.2 Sail-on-Service
26(3)
3.3.3 Inducement and Omission
29(1)
3.3.4 Cargo Demands
29(1)
3.3.5 Equipment
30(1)
3.3.6 Flexible Visitations
31(1)
3.4 Asia-CA3 Case Study
31(1)
3.5 Related Problems
32(2)
3.5.1 Shipping Problems
32(2)
3.5.2 Vehicle Routing Problems
34(1)
3.5.3 Airline Disruption Management
34(1)
3.6
Chapter Summary
34(1)
4 Methodological Background
35(18)
4.1 Automated Planning
35(2)
4.2 Partial-Order Planning
37(3)
4.3 Linear and Mixed-Integer Programming
40(5)
4.3.1 LP Duality
41(1)
4.3.2 Mixed-Integer Programming
41(4)
4.4 Constraint Programming
45(4)
4.4.1 CSPs and COPs
45(1)
4.4.2 Solving CP Problems
46(3)
4.5 Metaheuristics
49(4)
4.5.1 Local Search
50(1)
4.5.2 Simulated Annealing
51(2)
5 Liner Shipping Fleet Repositioning Without Cargo
53(36)
5.1 Dataset
54(1)
5.2 A PDDL Model of Fleet Repositioning
55(12)
5.2.1 PDDL Model
55(3)
5.2.2 Planners
58(1)
5.2.3 POPF
59(6)
5.2.4 PDDL Model Computational Evaluation in POPF
65(2)
5.3 Temporal Optimization Planning
67(9)
5.3.1 Linear Temporal Optimization Planning
70(2)
5.3.2 Domain Independent Heuristic Cost Estimation
72(1)
5.3.3 LTOP Model
73(1)
5.3.4 Fleet Repositioning Specific Heuristics
74(1)
5.3.5 LTOP Computational Evaluation
74(2)
5.4 A Mixed-Integer Programming Model of Fleet Repositioning
76(3)
5.4.1 Graph and MIP Description
76(2)
5.4.2 MIP Model Computational Evaluation in CPLEX
78(1)
5.5 A Constraint Programming Model of Fleet Repositioning
79(8)
5.5.1 Model Description
79(6)
5.5.2 CP Model Computational Evaluation in G12
85(2)
5.6
Chapter Summary
87(2)
6 Liner Shipping Fleet Repositioning with Cargo
89(52)
6.1 Graph Construction
90(6)
6.1.1 Phase-Out
91(1)
6.1.2 Phase-In
91(1)
6.1.3 Flexible Visitations
92(1)
6.1.4 Sail-on-Service
92(2)
6.1.5 Sailing Cost
94(1)
6.1.6 Graph Formalization
94(2)
6.2 Arc Flow Model
96(3)
6.2.1 Parameters
96(1)
6.2.2 Variables
97(1)
6.2.3 Objective and Constraints
97(2)
6.3 Path-Based Model
99(3)
6.3.1 Master Problem
100(1)
6.3.2 Sub-problem
101(1)
6.3.3 Reduced Graph
101(1)
6.4 LSFRP with Inflexible Visitations
102(6)
6.4.1 Preprocessing
105(1)
6.4.2 Equipment as Flows
105(2)
6.4.3 Equipment as Demands
107(1)
6.5 Heuristic Approaches
108(10)
6.5.1 Simulated Annealing
108(2)
6.5.2 Late Acceptance Hill Climbing
110(1)
6.5.3 Solution Representation
111(1)
6.5.4 Initial Solution Generation
111(3)
6.5.5 Neighborhoods
114(1)
6.5.6 Objective Evaluation
115(3)
6.6 Computational Complexity
118(1)
6.7 Computational Evaluation
118(20)
6.7.1 Dataset
119(1)
6.7.2 Arc, Node and Path Flow Approach Evaluations
120(3)
6.7.3 SA and LAHC Implementations
123(4)
6.7.4 Initial Solution Heuristics Comparison
127(1)
6.7.5 Neighborhood Analysis
128(2)
6.7.6 SA and LAHC Results
130(7)
6.7.7 Reference Instance Performance
137(1)
6.8
Chapter Summary
138(3)
7 Conclusion
141(2)
7.1 Outlook
142(1)
A No Cargo LSFRP PDDL Domain
143(22)
A.1 PDDL Model
143(11)
A.1.1 Predicates
143(2)
A.1.2 Functions
145(2)
A.1.3 Time
147(1)
A.1.4 Initial and Goal States
147(1)
A.1.5 Actions
148(6)
A.2 Forward PDDL Domain
154(5)
A.3 Reversed PDDL Domain
159(6)
B An LTOP Model of Fleet Repositioning
165(8)
B.1 Constants
165(1)
B.2 State Variables
166(1)
B.3 Optimization Variables
166(1)
B.4 Initial and Goal States
166(1)
B.5 Actions
167(6)
B.5.1 Phase-Out
167(1)
B.5.2 Phase-In
168(2)
B.5.3 Sailing
170(1)
B.5.4 Sailing with Equipment
171(1)
B.5.5 Sail-on-Service
171(2)
References 173(8)
Index 181
Kevin Tierney is currently an Assistant Professor of "Decision Support Systems and Operations Research" in the department of Business Information Systems at the University of Paderborn in Paderborn, Germany. He earned his PhD from the IT University of Copenhagen in Copenhagen, Denmark in 2013 for his work on optimizing liner shipping fleet repositioning plans as part of the ENERPLAN project. He holds a Sc.M. in Computer Science from Brown University (2010) and a B.S. in Computer Science from the Rochester Institute of Technology (2008).

Kevin Tierney researches the modeling and solution of large scale, real world optimization problems using mixed-integer linear programming, constraint programming and (meta)heuristics. He focuses in particular on problems in the maritime industry, including liner shipping networks and container terminal optimization.
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