On solution method for possibilistic optimization problem of one class with parameters characterized by quasiconcave upper semicontinuous strictly unimodal distribution functions

The problem of possibilistic level optimization with parameters characterized by quasiconcave upper semicontinuous strictly unimodal distribution functions is studied. The equivalent crisp analogue is constructed for the problem. We use the weakest and the strongest triangular norms in order to aggregate fuzzy information. Results obtained in the article generalize the case when parameters of the task are characterized by parameterized fuzzy numbers of $(L,R)$-type.