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Distributionally robust maximum probability shortest path problem

Author

Listed:
  • Rashed Khanjani-Shiraz

    (University of Tabriz)

  • Ali Babapour-Azar

    (University of Tabriz)

  • Zohreh Hosseini-Noudeh

    (University of Tabriz)

  • Panos M. Pardalos

    (University of Florida)

Abstract

In this study, we discuss and develop a distributionally robust joint chance-constrained optimization model and apply it for the shortest path problem under resource uncertainty. In sch a case, robust chance constraints are approximated by constraints that can be reformulated using convex programming. Since the issue we are discussing here is of the multi-resource type, the resource related to cost is deterministic; however, we consider a robust set for other resources where covariance and mean are known. Thus, the chance-constrained problem can be expressed in terms of a cone constraint. In addition, since our problem is joint chance-constrained optimization, we can use Bonferroni approximation to divide the problem into L separate problems in order to build convex approximations of distributionally robust joint chance constraints. Finally, numerical results are presented to illustrate the rigidity of the bounds and the value of the distributionally robust approach.

Suggested Citation

  • Rashed Khanjani-Shiraz & Ali Babapour-Azar & Zohreh Hosseini-Noudeh & Panos M. Pardalos, 2022. "Distributionally robust maximum probability shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 140-167, January.
    • Handle: RePEc:spr:jcomop:v:43:y:2022:i:1:d:10.1007_s10878-021-00747-9
    DOI: 10.1007/s10878-021-00747-9
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    References listed on IDEAS

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    1. Zohreh Hosseini Nodeh & Ali Babapour Azar & Rashed Khanjani Shiraz & Salman Khodayifar & Panos M. Pardalos, 2020. "Joint chance constrained shortest path problem with Copula theory," Journal of Combinatorial Optimization, Springer, vol. 40(1), pages 110-140, July.
    2. Hua Sun & Ziyou Gao & W. Szeto & Jiancheng Long & Fangxia Zhao, 2014. "A Distributionally Robust Joint Chance Constrained Optimization Model for the Dynamic Network Design Problem under Demand Uncertainty," Networks and Spatial Economics, Springer, vol. 14(3), pages 409-433, December.
    3. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    4. Yoshio Ohtsubo, 2003. "Minimizing risk models in stochastic shortest path problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 57(1), pages 79-88, April.
    5. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    6. G. C. Calafiore & L. El Ghaoui, 2006. "On Distributionally Robust Chance-Constrained Linear Programs," Journal of Optimization Theory and Applications, Springer, vol. 130(1), pages 1-22, July.
    7. Aharon Ben-Tal & Marc Teboulle, 1986. "Expected Utility, Penalty Functions, and Duality in Stochastic Nonlinear Programming," Management Science, INFORMS, vol. 32(11), pages 1445-1466, November.
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    Cited by:

    1. Belleh Fontem, 2023. "Robust Chance-Constrained Geometric Programming with Application to Demand Risk Mitigation," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 765-797, May.
    2. Hosseini-Nodeh, Zohreh & Khanjani-Shiraz, Rashed & Pardalos, Panos M., 2023. "Portfolio optimization using robust mean absolute deviation model: Wasserstein metric approach," Finance Research Letters, Elsevier, vol. 54(C).

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