Robust Possibilistic Optimization with Copula Function

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.

Automated summary

This paper discusses a linear optimization problem with uncertain objective function coefficients modeled by possibility distributions and applies a fuzzy robust optimization framework to compute a solution. By considering the dependencies between objective coefficients using a family of copula functions, it is shown that this new approach limits the conservatism of fuzzy robust optimization, evaluates possibility distributions for the objective function values more accurately, and does not increase the complexity of the problem.