Liner Ship Fleet Planning: Models and Algorithms

ISBN-13: 9780128115022 / Angielski / Miękka / 2017 / 204 str.

Tingsong Wang; Shuaian Wang; Qiang Meng

Opis

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

Spis treści

Part I: Introduction1. Introduction to Shipping Services2. Liner Ship Fleet Planning

Part II: Mathematical Modelling3. Introduction to Stochastic Programming4. Chance Constrained Programming5. Two-Stage Stochastic Model

Part III: Solution Algorithms6. Sample Average Approximation7. Dual Decomposition and Lagrangian Relaxation

Part IV: Case Studies8. Liner Ship Fleet Planning Problem with Individual Chance-Constrained Service Level9. Liner Ship Fleet Planning Problem with Joint Chance-Constrained Service Level10. Liner Ship Fleet Planning with Expected-Profit Maximization11. Multi-Period Liner Ship Fleet Planning

Part V: Conclusion12. Conclusions and Future Outlook

O autorze

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. 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. 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.