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Globalized Distributionally Robust Counterpart

Author

Listed:
  • Feng Liu

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Zhi Chen

    (Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon Tong, Hong Kong)

  • Shuming Wang

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

Abstract

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness , and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ -divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation.

Suggested Citation

  • Feng Liu & Zhi Chen & Shuming Wang, 2023. "Globalized Distributionally Robust Counterpart," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1120-1142, September.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:5:p:1120-1142
    DOI: 10.1287/ijoc.2022.0274
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    References listed on IDEAS

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    Cited by:

    1. Yueyao Li & Wenxun Xing, 2024. "Globalized distributionally robust optimization based on samples," Journal of Global Optimization, Springer, vol. 88(4), pages 871-900, April.

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