An Integrated MOGA Approach to Determine the Pareto-optimal Kanban Number and Size for a JIT System


In a just-in-time (JIT) system, kanban number and size represent the inventory level of work-in-process (WIP) or purchasing parts. It is an important issue to determine the feasible kanban number and size. In this research, an integrated multiple-objective genetic algorithm (MOGA) based system is developed to determine the Pareto-optimal kanban number and size, and is applied in a JIT-oriented manufacturing company to demonstrate its feasibility. In the integrated system, a simulation model is built to simulate the multi-stage JIT production system of the company. Then an experimental design of different kanban numbers and sizes for different production stages is applied to test the production performances. Based on the simulation results, regression models are built to represent the relationships between the kanban numbers of different production stages and the production performance. These regression models are then used in genetic algorithms to generate the performance for chromosomes. Finally, the proposed multi-objective genetic algorithm (MOGA) based system uses the generalized Parato-based scale independent fitness function (GPSIFF) as the fitness function to evaluate the multiple objectives for chromosomes and used to find the Pareto-optimal kanban number and size for multiple objectives, i.e., maximizing mean throughput rate and minimizing mean total WIP inventory. A comparison in the performance of the proposed system with that of the current kanban number is conducted to demonstrate the feasibility of the proposed system.