A genetic algorithm based stochastic simulation approach to chance constrained interval valued multiobjective decision making problems

This article presents how the stochastic simulation through genetic algorithm (GA) can be used to modeling and solving chance constrained interval valued multiobjective decision making (MODM) problems. In the proposed method, a stochastic simulation approach to the chance constraints is employed for interval valued goal representation of the objectives as well as decision identification through the use of an GA method in an inexact decision making context. In the executable goal programming (GP) model of the problem, both the aspects of the GP, minsum GP and minmax GP, are addressed within goal achievement function for minimizing possible regrets associated with the deviational variables of the defined goals for goal achievement within the target intervals specified in the decision making environment. A numerical example is solved and a comparison is made with the conventional GP approach.