Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems

Abstract

In this work, we deal with obtaining efficient solutions for stochastic multiobjective
programming problems. In general, these solutions are obtained in two stages: in one of them,
the stochastic problem is transformed into its equivalent deterministic problem, and in the other
one, some of the existing generating techniques in multiobjective programming are applied to
obtain efficient solutions, which involves transforming the multiobjective problem into a
problem with only one objective function. Our aim is to determine whether the order in which
these two transformations are carried out influences, in any way, the efficient solution obtained.
Our results show that depending on the type of stochastic criterion followed and the statistical
characteristics of the initial problem, the order can have an influence on the final set of efficient
solutions obtained for a given problem.