Mathematics (Mar 2023)

The New Solution Concept to Ill-Posed Bilevel Programming: Non-Antagonistic Pessimistic Solution

  • Xiang Li,
  • Tiesong Hu,
  • Xin Wang,
  • Ali Mahmoud,
  • Xiang Zeng

DOI
https://doi.org/10.3390/math11061422
Journal volume & issue
Vol. 11, no. 6
p. 1422

Abstract

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It is hardly realistic to assume that, under all decision circumstances, followers will always choose a solution that leads to the worst upper-level objective functional value. However, this generally accepted concept of the pessimistic solution to the ill-posed bilevel programming problems may lead to the leader’s attitude being more pessimistic vis à vis his anticipation of the follower’s decision being non-antagonistic. It will result in a wrong pessimistic solution and a greater potential of cooperation space between the leader and the followers. This paper presents a new concept of a non-antagonistic pessimistic solution with four numerical examples for bilevel programming problems from a non-antagonistic point of view. We prove that the objective function value of the non-antagonistic pessimistic solution generally dominates or is equal to the objective functional value of the pessimistic solution and the rewarding solution, and the maximum potential space for leader-follower cooperation can be overestimated in a generally applied pessimistic solution. Our research extends the concept of the pessimistic solution. It also sheds light on the insights that the non-antagonistic pessimistic solution can describe the practical potential of cooperation space between the leader and followers in non-antagonistic circumstances.

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