Modelling Empty Container Repositioning Logistics

ISBN-13: 9783030933821 / Angielski / Twarda / 2022 / 178 str.

Dong-Ping Song; Jingxin Dong

Spis treści

Part I (Chaps. 1),

Chapter 1. Container logistics chain and empty container repositioning (ECR)

Maritime logistics and container logistics

Container logistics chain

Importance of empty container repositioning

Reasons for empty container repositioning

Modelling methods for empty container repositioning

References

In Part II (Chaps. 2~7),

Chapter 2. Closed-form optimal ECR policy in a single depot with random demand 

Introduction

A fluid flow model based on continuous-time dynamic programming

Structural properties of the optimal policy

Solving the Hamilton–Jacobi–Bellman equations

Extension to more general cases

Numerical examples

Summary and notes

References

Chapter 3. Optimal ECR policy in two-depot stochastic systems: periodic-review

Introduction

A discrete stochastic dynamic programming model

Optimal ECR policy and its structural properties

Near-optimal threshold policy

Numerical examples

Summary and notes

References

Chapter 4. Optimal ECR policy in two-depot stochastic systems: continuous-review

Introduction

Discounted cost case

Convert into discrete-time Markov decision process

Optimal ECR policy and its structural properties

Closed-form objective function and optimal threshold values

Numerical examples

Long-run average cost case

Convert into discrete-time Markov decision process

Stationary distribution under threshold control policy

Optimality of threshold control policy

Numerical examples

Summary and notes

References

Chapter 5. Optimal and near-optimal ECR policies in hub-and-spoke stochastic systems

Introduction 

Convert into discrete-time Markov decision process

Optimal ECR policy

Suboptimal policy using a dynamic decomposition procedure

Numerical examples

Summary and notes

References

Chapter 6. Container sharing and ECR in two-depot stochastic systems

Introduction

Optimal ECR policy without container sharing

Optimal ECR policy with container sharing

Practical ECR policies 

Numerical examples

Summary and notes

References

Chapter 7. Optimal ECR in general inland transport systems with uncertainty

Introduction

Chance-constrained programming model

Robust optimisation model

Inventory control model

Summary and notes

References

Part III. (Chaps. 8~15),

Chapter 8. Container fleet sizing and ECR in shipping route with uncertain demands

Introduction

Problem formulation

Solution methods

Parameterized rule-based policy

Heuristic policy

Simulation-based evolutionary optimisation

Case studies

Summary and notes

References

Chapter 9. Container fleet sizing and ECR in shipping service considering inland transport times with uncertainty

Introduction

Problem formulation

A rule-based operational policy

Simulation-based optimisation

Case studies

Summary and notes

References

Chapter 10. Container lease term optimisation and ECR in shipping route with uncertain demand

Introduction

Problem description

Container lease term optimisation model

Operational rules in dynamic shipping systems

Solution procedure to optimise lease terms

Case studies

Summary and notes

References

Chapter 11. Evaluate flexible destination port ECR policy in shipping route with uncertain demand

Introduction

Problem formulation

Flexible destination port ECR policy

Determined destination port ECR policy

Case studies

Summary and notes

References

Chapter 12. Laden container routing and ECR in shipping network with multiple service routes

Introduction

Problem formulation

Solution methods

Shortest-path based integer programming solution

Heuristic-rules based integer programming solution

Numerical examples

Summary and notes

References

Chapter 13. Discrete-event driven simulation model for laden container distribution and ECR in shipping network 

Introduction

The operation of container shipping systems

Control policy for empty container management

Data design and simulation model structure

Summary and notes

References

Chapter 14. Evaluate ECR policies in liner shipping systems using simulation model 

Introduction

Inventory control-based policies for ECR

OD-flow matrix-based policies for ECR

Comparison of policy implementation

Comparison via numerical examples

Summary and notes

References

Chapter 15. Conclusions

Conclusions and managerial insights

Limitations and further research

References

O autorze

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.

Blurb

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.