Combining system dynamics and multi-objective optimization with design space reduction

Аннотация

Purpose – The purpose of this paper is to introduce an effective methodology of obtaining Perotoptimal solutions when combining system dynamics (SD) and multi-objective optimization (MOO) for supply chain problems. Design/methodology/approach – This paper proposes a new approach that combines SD and MOO within a simulation-based optimization framework for generating the efficient frontier for supporting decision making in supply chain management (SCM). It also addresses the issue of the curse of dimensionality, commonly found in practical optimization problems, through design space reduction. Findings – The integrated MOO and SD approach has been shown to be very useful for revealing how the decision variables in the Beer Game (BG) affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect (BWE). The results from the in-depth BG study clearly show that these three optimization objectives are in conflict with each other, in the sense that a supply chain manager cannot minimize the BWE without increasing the total inventory and total backlog levels. Practical implications – Having a methodology that enables effective generation of optimal tradeoff solutions, in terms of computational cost, time as well as solution diversity and intensification, assist decision makers in not only making decision in time but also present a diverse and intense solution set to choose from