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Robust possibilistic optimization with copula function

Abstract : This paper deals with a linear optimization problem with uncertain objective function coefficients modeled by possibility distributions. The fuzzy robust optimization framework is applied to compute a solution. Namely, the necessity degree that the objective value is lower than a given threshold is maximized. The aim of this paper is to take the knowledge on dependencies between the objective coefficients into account by means of a family of copula functions. It is shown that this new approach limits the conservatism of fuzzy robust optimization, better evaluates possibility distributions for the values of the objective function and do not increase the complexity of the problem.
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https://hal.archives-ouvertes.fr/hal-03324820
Contributor : Romain Guillaume Connect in order to contact the contributor
Submitted on : Wednesday, August 25, 2021 - 11:41:34 AM
Last modification on : Wednesday, August 25, 2021 - 4:19:15 PM

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Romain Guillaume, Adam Kasperski, Pawel Zielinski. Robust possibilistic optimization with copula function. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Jul 2021, Luxembourg, Luxembourg. ⟨10.1109/FUZZ45933.2021.9494572⟩. ⟨hal-03324820⟩

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