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Distributionally Robust Optimization in Possibilistic Setting

Abstract : In this paper a class of optimization problems with uncertain constraint coefficients is discussed. Namely, for each ill-known coefficient a possibility distribution, being a membership function of a fuzzy interval, is specified. In a possibilistic interpretation, the induced possibility distribution in the set of constraint coefficient realizations encodes a family of probability distributions in this set. The distributionally robust approach is then used to transform imprecise constraints into crisp counterparts. An extension of the model is proposed, in which individual risk aversion of decision makers is taken into account.
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Contributor : Romain Guillaume Connect in order to contact the contributor
Submitted on : Wednesday, September 1, 2021 - 9:53:05 AM
Last modification on : Friday, September 3, 2021 - 3:36:21 AM


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Romain Guillaume, Adam Kasperski, Pawel Zielinski. Distributionally Robust Optimization in Possibilistic Setting. International Conference on Fuzzy Systems (FUZZ-IEEE 2021), IEEE, Jul 2021, Luxembourg, Luxembourg. pp.1-6, ⟨10.1109/FUZZ45933.2021.9494390⟩. ⟨hal-03330582⟩



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