A Simulation Based Fuzzy Programming Approach to SolveDiscrete Multiobjective Chance ConstrainedProgramming Problems

摘要

A simulation based fuzzy programming approach is presented for solving a class of multiobjective programming (MOP) problems. The problem considered is a MOP in which some or all the constraints are chance constraints involving discrete random variables on both sides of constraints. Classical MOP techniques are not directly applicable to solve this type of problems as the deterministic equivalents of chance constraints are not possible to derive. Simulation based techniques are useful in such cases. In this work, Monte-Carlo simulation is used to estimate the probabilities and guarantee the feasibility of solutions. It is combined with a genetic algorithm (GA) which is used as the search technique. Fuzzy programming formulation of the problem is developed first and then the compromise solution is obtained using the GA. The proposed method provides a compromise solution to the given MOP. It is applied to a numerical test problem.