Liner Ship Fleet Planning: Models and Algorithms systematically introduces the latest research on modeling and optimization for liner ship fleet planning with demand uncertainty. Container shipping companies have struggled since the financial crisis of 2007-2008, making it critical for them to make informed decisions about their fleet planning and development. Current and future shipping professionals require systematic approaches for investigating and solving their fleet planning problems, as well as methodologies for addressing their other shipping responsibilities. Liner Ship Fleet Planning addresses these needs, providing the most recent quantitative research of liner shipping in maritime transportation. The research and methods provided assist those tasked with optimizing shipping efficiency and fleet deployment in the face of uncertain demand. Suitable for those with any level of quantitative background, the book serves as a valuable resource for both maritime academics, and shipping professionals involved in planning and scheduling departments. - Introduces the latest research on maritime transportation problems - Analyzes problems of liner ship fleet planning, taking uncertainty into account - Promotes the use of mathematics to manage uncertainty, using stochastic programming models, and proposing solution algorithms to solve proposed models - Includes case studies that provide detailed examples of real-world examples of fleet optimization - Explains how stochastic programming modeling methods and solution algorithms can be applied to other research fields featuring uncertainty, such as container yard planning, berth allocation and vehicle deployment problems

Tingsong Wang is an Associate Professor in the Department of Economics and Management at Wuhan University (China). His research interests include maritime transportation, network design and optimization, with a focus on modelling and algorithm design for liner ship fleet planning problems.

Liner Ship Fleet Planning: Models and Algorithms systematically introduces the latest research on modeling and optimization for liner ship fleet planning with demand uncertainty. Container shipping companies have struggled since the financial crisis of 2007-2008, making it critical for them to make informed decisions about their fleet planning and development.

Current and future shipping professionals require systematic approaches for investigating and solving their fleet planning problems, as well as methodologies for addressing their other shipping responsibilities. Liner Ship Fleet Planning addresses these needs, providing the most recent quantitative research of liner shipping in maritime transportation. The research and methods provided assist those tasked with optimizing shipping efficiency and fleet deployment in the face of uncertain demand. Suitable for those with any level of quantitative background, the book serves as a valuable resource for both maritime academics, and shipping professionals involved in planning and scheduling departments.

• Introduces the latest research on maritime transportation problems
• Analyzes problems of liner ship fleet planning, taking uncertainty into account
• Promotes the use of mathematics to manage uncertainty, using stochastic programming models, and proposing solution algorithms to solve proposed models
• Includes case studies that provide detailed examples of real-world examples of fleet optimization
• Explains how stochastic programming modeling methods and solution algorithms can be applied to other research fields featuring uncertainty, such as container yard planning, berth allocation and vehicle deployment problems

Part I: Introduction 1. Introduction to Shipping Services 2. Liner Ship Fleet Planning

Part II: Mathematical Modelling 3. Introduction to Stochastic Programming 4. Chance Constrained Programming 5. Two-Stage Stochastic Model

Part III: Solution Algorithms 6. Sample Average Approximation 7. Dual Decomposition and Lagrangian Relaxation

Part IV: Case Studies 8. Liner Ship Fleet Planning Problem with Individual Chance-Constrained Service Level 9. Liner Ship Fleet Planning Problem with Joint Chance-Constrained Service Level 10. Liner Ship Fleet Planning with Expected-Profit Maximization 11. Multi-Period Liner Ship Fleet Planning

Part V: Conclusion 12. Conclusions and Future Outlook