Modelling Empty Container Repositioning Logistics

 
 
Springer (Verlag)
  • erscheint ca. am 1. März 2022
 
  • Buch
  • |
  • Hardcover
  • |
  • X, 180 Seiten
978-3-030-93382-1 (ISBN)
 

The book takes the inventory control perspective to tackle empty container repositioning logistics problems in regional transportation systems by explicitly considering the features such as demand imbalance over space, dynamic operations over time, uncertainty in demand and transport, and container leasing phenomenon. The book has the following unique features. First, it provides a discussion of broad empty equipment logistics including empty freight vehicle redistribution, empty passenger vehicle redistribution, empty bike repositioning, empty container chassis repositioning, and empty container repositioning (ECR) problems. The similarity and unique characteristics of ECR compared to other empty equipment repositioning problems are explained. Second, we adopt the stochastic dynamic programming approach to tackle the ECR problems, which offers an algorithmic strategy to characterize the optimal policy and captures the sequential decision-making phenomenon in anticipation of uncertainties over time and space. Third, we are able to establish closed-form solutions and structural properties of the optimal ECR policies in relatively simple transportation systems. Such properties can then be utilized to construct threshold-type ECR policies for more complicated transportation systems. In fact, the threshold-type ECR policies resemble the well-known (s, S) and (s, Q) policies in inventory control theory. These policies have the advantages of being decentralized, easy to understand, easy to operate, quick response to random events, and minimal on-line computation and communication. Fourth, several sophisticated optimization techniques such as approximate dynamic programming, simulation-based meta-heuristics, stochastic approximation, perturbation analysis, and ordinal optimization methods are introduced to solve the complex stochastic optimization problems.

The book will be of interest to researchers and professionals in logistics, transport, supply chain, and operations research.

1st ed. 2022
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • Für Beruf und Forschung
  • 27
  • |
  • 6 farbige Abbildungen, 27 s/w Abbildungen
  • |
  • 6 Illustrations, color; 27 Illustrations, black and white; X, 180 p. 33 illus., 6 illus. in color.
  • Höhe: 23.5 cm
  • |
  • Breite: 15.5 cm
978-3-030-93382-1 (9783030933821)
10.1007/978-3-030-93383-8
weitere Ausgaben werden ermittelt

Dr Dong-Ping Song is a professor of Supply Chain Management in the School of Management at the University of Liverpool. He is a Senior Member of IEEE; an Associate Editor for Transportation Research Part E and International Journal of Shipping and Transport Logistics. His research interests include the applications of mathematical modeling, data analytics, artificial intelligence, and simulation-based tools to various supply chain, logistics, and transportation systems, particularly in the area of maritime transport. He has published four books titled as "Optimal Control and Optimization of Stochastic Supply Chain Systems" by Springer (ISBN 9781447147237); "Optimising Supply Chain Performance: Information Sharing and Coordinated Management" by Palgrave Macmillan (ISBN 9781137501134); "Container Logistics and Maritime Transport" by Routledge (ISBN 9780367336509); and "Dual-Channel Supply Chain Decisions with Risk-Averse Behaviour" by World Scientific Publishing (ISBN 9781800610392). He has had papers published in international journals including IEEE Transactions on Automatic Control, Transportation Research Part B/E/D, European Journal of Operational Research, and Naval Research Logistics.

Dr Jing-Xin Dong is a professor of Operations Management and Supply Chain Management at Newcastle University Business School, UK. His research interests include operations research, port and shipping management, intelligent logistics and transport, and GPS/GIS. He is an associate editor for IET Intelligent Transport System and a member of Editorial Board for European Management Journal. He has co-authored a chapter "Empty Container Repositioning" in the book "Handbook of Ocean Container Transport Logistics" published by Springer (ISBN 9783319118901). He has published papers in Transportation Research Part B/E/D, European Journal of Operational Research, International Journal of Production Economics, Annals of Operations Research, etc.

Part I (Chaps. 1),Chapter 1. Container logistics chain and empty container repositioning (ECR)Maritime logistics and container logisticsContainer logistics chainImportance of empty container repositioningReasons for empty container repositioningModelling methods for empty container repositioningReferences
In Part II (Chaps. 2~7),Chapter 2. Closed-form optimal ECR policy in a single depot with random demand IntroductionA fluid flow model based on continuous-time dynamic programmingStructural properties of the optimal policy Solving the Hamilton-Jacobi-Bellman equationsExtension to more general casesNumerical examples Summary and notesReferences
Chapter 3. Optimal ECR policy in two-depot stochastic systems: periodic-reviewIntroductionA discrete stochastic dynamic programming modelOptimal ECR policy and its structural propertiesNear-optimal threshold policyNumerical examplesSummary and notesReferences
Chapter 4. Optimal ECR policy in two-depot stochastic systems: continuous-reviewIntroductionDiscounted cost caseConvert into discrete-time Markov decision processOptimal ECR policy and its structural propertiesClosed-form objective function and optimal threshold valuesNumerical examplesLong-run average cost caseConvert into discrete-time Markov decision processStationary distribution under threshold control policyOptimality of threshold control policyNumerical examplesSummary and notesReferences
Chapter 5. Optimal and near-optimal ECR policies in hub-and-spoke stochastic systemsIntroduction Convert into discrete-time Markov decision processOptimal ECR policySuboptimal policy using a dynamic decomposition procedureNumerical examplesSummary and notesReferences
Chapter 6. Container sharing and ECR in two-depot stochastic systemsIntroductionOptimal ECR policy without container sharingOptimal ECR policy with container sharingPractical ECR policies Numerical examplesSummary and notesReferences
Chapter 7. Optimal ECR in general inland transport systems with uncertaintyIntroductionChance-constrained programming modelRobust optimisation modelInventory control modelSummary and notesReferences

Part III. (Chaps. 8~15),Chapter 8. Container fleet sizing and ECR in shipping route with uncertain demandsIntroductionProblem formulationSolution methodsParameterized rule-based policyHeuristic policySimulation-based evolutionary optimisationCase studiesSummary and notesReferences
Chapter 9. Container fleet sizing and ECR in shipping service considering inland transport times with uncertaintyIntroductionProblem formulationA rule-based operational policySimulation-based optimisationCase studiesSummary and notesReferences
Chapter 10. Container lease term optimisation and ECR in shipping route with uncertain demandIntroductionProblem descriptionContainer lease term optimisation modelOperational rules in dynamic shipping systemsSolution procedure to optimise lease termsCase studiesSummary and notesReferences
Chapter 11. Evaluate flexible destination port ECR policy in shipping route with uncertain demandIntroductionProblem formulationFlexible destination port ECR policyDetermined destination port ECR policyCase studiesSummary and notesReferences
Chapter 12. Laden container routing and ECR in shipping network with multiple service routesIntroductionProblem formulationSolution methodsShortest-path based integer programming solutionHeuristic-rules based integer programming solutionNumerical examplesSummary and notesReferences
Chapter 13. Discrete-event driven simulation model for laden container distribution and ECR in shipping network IntroductionThe operation of container shipping systemsControl policy for empty container managementData design and simulation model structureSummary and notesReferences
Chapter 14. Evaluate ECR policies in liner shipping systems using simulation model IntroductionInventory control-based policies for ECROD-flow matrix-based policies for ECRComparison of policy implementationComparison via numerical examplesSummary and notesReferences
Chapter 15. ConclusionsConclusions and managerial insightsLimitations and further researchReferences
The book takes the inventory control perspective to tackle empty container repositioning logistics problems in regional transportation systems by explicitly considering the features such as demand imbalance over space, dynamic operations over time, uncertainty in demand and transport, and container leasing phenomenon. The book has the following unique features. First, it provides a discussion of broad empty equipment logistics including empty freight vehicle redistribution, empty passenger vehicle redistribution, empty bike repositioning, empty container chassis repositioning, and empty container repositioning (ECR) problems. The similarity and unique characteristics of ECR compared to other empty equipment repositioning problems are explained. Second, we adopt the stochastic dynamic programming approach to tackle the ECR problems, which offers an algorithmic strategy to characterize the optimal policy and captures the sequential decision-making phenomenon in anticipation of uncertainties over time and space. Third, we are able to establish closed-form solutions and structural properties of the optimal ECR policies in relatively simple transportation systems. Such properties can then be utilized to construct threshold-type ECR policies for more complicated transportation systems. In fact, the threshold-type ECR policies resemble the well-known (s, S) and (s, Q) policies in inventory control theory. These policies have the advantages of being decentralized, easy to understand, easy to operate, quick response to random events, and minimal on-line computation and communication. Fourth, several sophisticated optimization techniques such as approximate dynamic programming, simulation-based meta-heuristics, stochastic approximation, perturbation analysis, and ordinal optimization methods are introduced to solve the complex stochastic optimization problems.



The book will be of interest to researchers and professionals in logistics, transport, supply chain, and operations research.

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