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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
Postgraduates and academics in maritime transportation, shipping management and maritime economics, Practitioners in planning and scheduling for shipping companies, Shipping and port government officials involved with policy-making and planning
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
- No. of pages:
- © Elsevier 2017
- 18th May 2017
- Paperback ISBN:
- eBook ISBN:
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.
Associate Professor, Department of Economics and Management, Wuhan University, China
Shuaian Wang is an Associate Professor in the Department of Logistics and Maritime Studies at Hong Kong Polytechnic University. His research interests include maritime transportation, container shipping and port operations, and transportation network modelling and analysis.
Associate Professor, Department of Logistics and Maritime Studies, Hong Kong Polytechnic University
Qiang Meng is a Professor in the Department of Civil and Environmental Engineering at National University of Singapore, and track leader on Shipping & Logistics Transportation in the Centre for Maritime Studies of National University of Singapore. His research expertise includes transportation network modelling and optimization, shipping network analysis and quantitative risk analysis.
Professor, Department of Civil and Environmental Engineering, National University of Singapore
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