Petri Net And Particle Swarm Optimization Algorithm Based Approach For JSP Scheduling Optimization

Scheduling is making the plan for the production process, which plays the leading role in the manufacture and transportation. Job shop scheduling is a kind of typical production scheduling, from the mathematical programming point of view, job shop scheduling problem can be expressed as: in the equation or inequality constraints, the optimization of the objective function. The kernel problem of job shop scheduling is the model and the algorithm, and effective scheduling model is the important part of the job shop scheduling. Effective scheduling methods can greatly improve the production benefit and utilization factor. By modeling and analyzing job shop scheduling, we can find the systems' faults and can diagnose and recover them in the process of real-time control more easily. Now, because Petri nets can describe the share, conflict, mutex, concurrence and uncertainty of resources and their model can be analyzed quantitatively and qualitatively. With traditional modeling, analysis and control methods, Petri nets as the graphical and mathematical modeling tools, can provide an integrated modeling, analysis and control environment, it can better describe the discrete events dynamic process, the design for the shop scheduling is convenience. Several kinds of Petri nets have become the important methods of modeling and scheduling job shop scheduling. Particle swarm optimization is the evolutionary algorithm based on swarm intelligence and the bionic algorithm that imitates the bird swarm to seek the food. Particle swarm optimization has the obvious computation model, and is easy to be carried out and has good optimization performance.The present dissertation uses Petri nets and Particle Swarm Optimization as a tool to study the job shop scheduling problems with multiple process plants. The algorithm is compared with other scheduling algorithms and is tested by standard benchmark scheduling problem. The result has shown that the proposed algorithm is correct and excellent.The principal research results and contents can be outlined as following:1. The scheduling Petri nets model could be applied to processing a variety of jobs. The model study a production plan for each job with multiple process plants, while ensuring selecting the optimal results of the production cycle, in accordance with the requirements to establish the corresponding Petri nets model.2. Based on mathematical and Petri net model, introduced an improved coding particle swarm algorithm based shop scheduling problem. The static and dynamic scheduling are optimized.3. Finally, achieved the proposed algorithm of job scheduling problem with matlab. The algorithm is compared with other scheduling algorithms. The result has shown that the proposed algorithm is correct and excellent.