A robust simulation-based multicriteria optimization methodology

This paper describes a methodology for solving parameter design (PD) problems in production and business systems of considerable complexity. The solution is aimed at determining optimum settings to system critical parameters so that each system response is at its optimum performance level with least amount of variability. When approaching such problem, analysts are often faced with four major challenges: representing the complex parameter design problem, utilizing an effective search method that is able to explore the problem's complex and large domain, making optimization decisions based on multiple and, often, conflicting objectives, and handling the stochastic variability of system response as an integral part of the search method. to tackle such challenges, this paper proposes a solution methodology that integrates four state-of-the-art modules of proven methods: simulation modeling (SM), genetic algorithm (GA), entropy method (EM), and robustness module (RM).