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Decision bounding problems for two-stage distributionally robust stochastic bilevel optimization

Author

Listed:
  • Xiaojiao Tong

    (Hunan First Normal University
    Xiangtan University)

  • Manlan Li

    (Xiangtan University)

  • Hailin Sun

    (Nanjing Normal University)

Abstract

Distributionally robust optimization (DRO) becomes a hot research topic in stochastic programming (SP) due to its characteristic for solving SP problems with incomplete distribution information. In this paper, we study a two-stage distributionally robust stochastic bilevel optimization problem (TDRBO) under the moment uncertainty. The TDRBO problem has a complicated optimization structure and is intractable. To this end, we first present the corresponding robust optimistic and pessimistic models as the bounds of the TDRBO problem. Then we use the decision rule approach to handle the bounded optimization problems under two situations of fixed recourse and random recourse. Finally, we obtain the tractable bounded optimization problems of the TDRBO problem. Some theoretical results are established and the numerical results are presented, showing that the approaches proposed in this paper are reasonable and effective.

Suggested Citation

  • Xiaojiao Tong & Manlan Li & Hailin Sun, 2023. "Decision bounding problems for two-stage distributionally robust stochastic bilevel optimization," Journal of Global Optimization, Springer, vol. 87(2), pages 679-707, November.
    • Handle: RePEc:spr:jglopt:v:87:y:2023:i:2:d:10.1007_s10898-022-01227-y
    DOI: 10.1007/s10898-022-01227-y
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    References listed on IDEAS

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    Cited by:

    1. Gao-Xi Li & Xin-Min Yang & Xian-Jun Long, 2025. "Partial augmented Lagrangian method for non-Lipschitz mathematical programs with complementarity constraints," Journal of Global Optimization, Springer, vol. 92(2), pages 345-379, June.

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