Pessimistic Referential-Uncooperative Linear Bilevel Multi-follower Decision Making with An Application to Water Resources Optimal Allocation

نویسندگان

  • Yue Zheng
  • Yuxin Fan
  • Xiangzhi Zhuo
  • Jiawei Chen
چکیده

Abstract: In this paper, we study a pessimistic linear bilevel multi-follower programming problem in a referential-uncooperative situation (for short, PLBMF-RU) where the followers are uncooperative while having cross reference to decision making information of the other followers. We first give a model and the solution definitions of PLBMF-RU, and then use a simple example to illustrate it. Furthermore, we develop an algorithm based on the penalty function method with finite termination to solve PLBMF-RU. Finally, a case study of water resources optimal allocation illustrates the feasibility of the proposed algorithm.

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تاریخ انتشار 2016