A hybrid GA-simulation approach to improve JIT systems

This study presents a hybrid approach involving genetic algorithms (GA) as an optimization search technique and a simulation model, for representing the dynamic behavior of the system and its limitations, to improve the practical JIT manufacturing system. To achieve the objective, first, the actual system is modeled and simulated (by considering the system¿s limitations and its dynamic behavior). Second, the integrated simulation model is tested and validated by analysis of variance. Third, the hybrid GA-Simulation approach is used in an interactive manner to determine the optimal number of kanban cards in different stations of the practical JIT system. The presented hybrid approach is tested and applied to an auto industry production line. Furthermore, it is compared with the practical JIT through analysis of variance (ANOVA) and the results show improvements in the average daily production rate, the average resource utilization and the average cycle time but some deterioration in the average queue length and in process inventory is inevitable.