Globalized Distributionally Robust Counterpart

Feng Liu (), Zhi Chen () and Shuming Wang ()
Additional contact information
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

INFORMS Journal on Computing, 2023, vol. 35, issue 5, 1120-1142

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.

Keywords: robust and distributionally robust optimization; robust satisficing; globalized robustness (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2022.0274 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:5:p:1120-1142

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().