A Petri Nets and Genetic Algorithm Based Optimal Scheduling for Job Shop Manufacturing Systems

An optimal production scheduling solution to meet the order is a must for enterprise to gain profit. This paper presents a novel Petri nets and Genetic Algorithm (PNGA) optimal scheduling method for job shop manufacturing systems. 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 results with the ordinary Genetic Algorithm (GA) and Hybrid Taguchi-Genetic Algorithm (HTGA) methods. The MATLAB software was adopted to model the Petri nets in this study. Taguchi's method was used to optimize these experiment 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 process 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 process 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 PNGA is able to provide a better production scheduling solution.