Optimization of multi-echelon supply chain kanban model using genetic algorithm

Abstract

A supply chain system (SCS) consists of organizations or companies that utilizes approaches to have an effective relation between suppliers, manufacturers, warehouses, distribution centers, retailers and finally, the customers. Kanban system is an efficient and easy system to be implemented. In a kanban system each plant sends signals to the following plant for needed parts. The kanban withdraws parts instead of pushing parts from one station to another station. The workstations are located along the production lines and only produce or deliver desired components when they receive a card. The number ofkanbans can significantly influence the load balance between processes and the amount of orders suppliers need to obtain from subcontractors. Large kanban size results in large amount of WIP inventory at each workstation. On the other side, although reducing kanban size causes a decrease in WIP inventory it leads to transportation increases as well as reduction in the system throughput rate. In this study a multi-echelon supply chain system controlled by kanban system, is considered. Decision making is based on determination of the number of kanbans as well as economic-quantity order of products. Since the adopted model used in this study is mixed integer non-linear programming (MINLP) type and solving it by exact algorithm such as branch and bound (B&B) takes a lot of time, a heuristic method via Genetic algorithm (GA) is presented. Some problems are solved by our proposed GA to illustrate the performance ofthe method.