Hybrid GA for material routing optimization in supply chain

Abstract

In designing a supply chain (SC) system, the problem arises when a company has unsatisfactory inventory control policy and material routing between supplier–producer and distributor in SC considering specified cost and demand. The integration of decisions of different functions into a single optimization model is the base of this research. The aim of this paper is to study and compare the existing models of supply, production and distribution in SC and propose a model which integrates mentioned criteria in supply chain management (SCM). Furthermore, it proposes a new method for calculation of fitness function in genetic algorithm (GA) process. The successful designing of this model has led us to explore the use of heuristic methods such as GA to quantify the flow of SC, information and material flow. At first, fuzzy analytic hierarchy process (FAHP) is adapted to evaluate objective function weights in SC. Then final weights of objective function are determined by the technique for order of preference by similarity to ideal solution (TOPSIS). This research also simulated the real company SC operations, and determines the most effective strategic and operational policies for an effective SC system. The result obtained from the model shows that it is robust. This model can also be applied to other industrial environments with slight modifications.