A reciprocated result using an approach of multiobjective stochastic linear programming models with partial uncertainty

This study focuses on solving multiobjective stochastic linear programming (MSLP) problems with partial information on probability distribution. The method presented in this paper utilises the concept of ranking function and linguistic hedges in the fuzzy transformation stage to take into account the internal values of the probability distribution. An adaptive arithmetic average approach is then used to convert the multiobjective problems into a single objective problem. Comparison of results with existing methods in the literature is presented which shows that the method presented performs as good as the existing methods in terms of solution quality but better in terms of computational effort