Tingsong Wang, Shuaian Wang, Qiang Meng

Liner Ship Fleet Planning

Models and Algorithms

• Broschiertes Buch

Produktbeschreibung

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 maritimeacademics, and shipping professionals involved in planning and scheduling departments.

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Produktdetails

• Verlag: Elsevier / Elsevier Science & Technology
• Artikelnr. des Verlages: C2016-0-00752-0
• Seitenzahl: 204
• Erscheinungstermin: 18. Mai 2017
• Englisch
• Abmessung: 228mm x 149mm x 15mm
• Gewicht: 450g
• ISBN-13: 9780128115022
• ISBN-10: 0128115025
• Artikelnr.: 48065249

Autorenporträt

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.

Inhaltsangabe

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