Research On Optimization And Simulation Of Production Scheduling Problem Based On Process Industry

Production scheduling problem in the process industry is one of the major concerns of processing enterprise, which plays a significant role in the production of the enterprise. Hybrid flow shop scheduling issue has been widely researched in the process of industrial production scheduling. Thus, the problem of hybrid flow shop scheduling has been a major research of this paper. The solving method for the traditional model is easy to fall into local optimal solution and premature convergence in the problem of hybrid flow shop production scheduling, which will influence the reasonable production scheduling plan enterprise made. If the entire production workshop has a machinery and equipment failure, which leads to the current scheduling scheme failure directly.In view of the above problem, particle swarm optimization algorithm in a series of improvement and Petri net is studied in this paper. By using an improved accelerating particle swarm algorithm to solve the hybrid flow shop scheduling model and combining of dynamic simulation capability of Petri nets, third-order hybrid flow shop machine failure model is established. Using the above method not only improves the precision of the model, but also achieves the study of fault model. In this paper, the main research work is as follows:(1) In order to improve the precision of the hybrid flow shop scheduling model, this paper investigates particle swarm optimization. In the particle swarm optimization algorithm, accelerated particle swarm algorithm can obviously improve the searching ability and convergence speed of standard particle swarm algorithm. But it’s easy to fall into local optimum and premature convergence. Improved accelerating particle swarm optimization algorithm not only ensures the population number of particles, but also avoids the problem of local optimum and premature convergence. In this article, through the comparison of related test function, the validity of the improved accelerating particle swarm optimization algorithm is proved.(2) By employing the acceleration of the improved particle swarm algorithm, we can obtain the solution of hybrid flow shop model. The study of particle swarm algorithm hybrid flow shop scheduling problem has many advantages. But the particle swarm self- limitativeness can’t satisfy the hybrid flow shop problem. By using the acceleration of the improved particle swarm algorithm, we solve the solution of the hybrid flow shop scheduling model regarding the maximum completion time as objective function. Through experimental comparison, we prove the effectiveness and advantages of the above method.(3) By applying Petri net model, this paper studies the hybrid flow shop scheduling machine failure problems. It is hard to model and simulate the dynamic characteristic of the hybrid flow shop and machine fault in the normal model. Therefore, by using the dynamic simulation of Petri nets, this paper build the simulation model based on Petri net.