The Optimum Job Shop Production Scheduling by Using Petri Nets and Genetic Algorithm

This study aims at exploring the job shop production scheduling optimization. A novel Petri nets and genetic algorithm (PNGA) is present. Using the job shop production of a mold factory as a case study, we examined the capability of the proposed PNGA method and compared its performance with the traditional Genetic Algorithm (GA) and Hybrid Taguchi-Genetic Algorithm (HTGA) methods. The programming software of MATLAB was employed to model the Petri nets in this study. Taguchi’s method was adopted to obtain the optimal experimental parameters. The optimal parameter settings were then programmed into the PNGA program. In conjunction with the Petri nets model, the process time was then estimated. The simulation results show that the average processing time of PNGA is about 287 (unit time). It is less than 289.55 of the GA and 288.8 of the HTGA. The standard deviation of processing time of PNGA is about 5.20. It is less than 6.0 of the GA and 5.88 of the HTGA. That is, the proposed Petri nets and genetic algorithm (PNGA) is able to provide a better job shop production scheduling optimization.