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Safe Approximations for Distributionally Robust Joint Chance Constrained Program

Author

Listed:
  • Chenchen Wu

    (College of Science, Tianjin University of Technology, Tianjin 300384, P. R. China)

  • Dachuan Xu

    (Department of Information and Operations Research, Beijing University of Technology, Beijing 100124, P. R. China)

  • Jiawei Zhang

    (Stern School of Business, New York University, New York, NY, 10012-1126, USA)

Abstract

In this paper, we present a bilinear second-order cone programming safe approximation for the distributionally robust chance constrained program (DRCCP), assuming that the support of the uncertain parameters, and the first and second marginal moments of the probability with respect to the interval constraint imposed on the sum of the uncertain parameters are given. If we further know the covariance matrix, we can obtain a bilinear semi-definite programming safe approximation. Preliminary numerical tests indicate that the proposed models are competitive.

Suggested Citation

  • Chenchen Wu & Dachuan Xu & Jiawei Zhang, 2015. "Safe Approximations for Distributionally Robust Joint Chance Constrained Program," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(01), pages 1-20.
    • Handle: RePEc:wsi:apjorx:v:32:y:2015:i:01:n:s0217595915400047
    DOI: 10.1142/S0217595915400047
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    Cited by:

    1. Fengmin Xu & Meihua Wang & Yu-Hong Dai & Dachuan Xu, 2018. "A sparse enhanced indexation model with chance and cardinality constraints," Journal of Global Optimization, Springer, vol. 70(1), pages 5-25, January.

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