Optimization of A Multi-Echelon Supply Chain Kanban Model Using Genetic Algorithm

Kanban is an efficient and easy way to implement the Just-in-time (JIT) production system in a supply chain. The number of kanbans can significantly influence the load balance between processes and the amount of orders that manufacturers need to obtain from suppliers. In this study, a multi-stage supply chain system (MSSCS) controlled by the kanban technique, is considered. Wang and Sarker (2006) developed a formula for a MSSCS based on the determination of the number of kanbans in each stage as well as the economic order quantity of products. Since the model used is a mixed integer non-linear programming (MINLP), solving it by exact algorithms such as branch and bound (B&B) takes a lot of time. In this study, a heuristic method via Genetic Algorithm (GA) is presented and some problems are solved by the proposed GA to illustrate its performance.