Journal of South China University of Technology (Natural Science Edition) ›› 2010, Vol. 38 ›› Issue (3): 82-88.doi: 10.3969/j.issn.1000-565X.2010.03.015

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Robust Optimization Model of Fleet Planning Decision Under Demand Uncertainty

Yang Qiu-ping  Xie Xin-lian  Su Chen   

  1. Transportation Management College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2009-07-14 Revised:2009-11-24 Online:2010-03-25 Published:2010-03-25
  • Contact: 杨秋平(1982-),女,博士生,主要从事交通运输规划与管理研究. E-mail:qpyang@126.com
  • About author:杨秋平(1982-),女,博士生,主要从事交通运输规划与管理研究.
  • Supported by:

    国家自然科学基金资助项目(50778029)

Abstract:

This paper deals with the fleet planning decision in complex and uncertain circumstances. In the investigation, first, the existing methods and basic characteristics of fleet planning are analyzed. Next, by combining the ship deployment optimization with the fleet development, a deterministic model of fleet planning with muhimode in- vestment, which mee.ts the actual demands of the complex market, is established. Then, by introducing the robust optimization approach based on scenario analysis and by using a scenario set with given probability to describe the uncertainty of market demands, the deterministic model is extended to a robust optimization model considering vari- ous uncertain factors. The new model, possessing strong robustness, considers not only the demand uncertainty but also various complex practical issues-the ship operating outcome, the enterprise investment capability, the buil- ding of new ships, the purchase or sale of second-hand ships and the ship chartering. Finally, by using a shipping enterprise as an example, the robust optimization model is compared with the deterministic one. The results indicate that the robust model helps to obtain relatively conservative solution and is effective in guaranteeing the robustness of fleet planning decision.

Key words: fleet planning, multimode investment, uncertainty, robust optimization

CLC Number: