Soft robust solutions to possibilistic optimization problems

This paper discusses a class of uncertain optimization problems, in which unknown parameters are modeled by fuzzy intervals. The membership functions the intervals interpreted as possibility distributions for values parameters. It is shown how known concepts robustness and light robustness, traditional interval uncertainty representation parameters, can be generalized to choose solutions that optimize against plausible parameter realizations under assumed model possibilistic setting. Furthermore, these computed efficiently wide particular linear programming problems with constraints objective function. Thus consideration not much computationally harder than their deterministic counterparts. In this theoretical framework presented results some computational tests shown.