Research On Hybrid Particle Swarm Algorithm For Solving Job Shop Scheduling Problems

The job shop scheduling problems has always been a key issue in the management of production enterprises,which directly affects the production efficiency and competitiveness of enterprises.In the workshop scheduling problems,the complexity of the problem is often very high,and there are often many conflicts and constraints.At the same time,there are many practical situations that need to be considered,such as labor costs and equipment losses.Therefore,the in-depth study of the workshop scheduling problems is conducive to better guiding the enterprises to arrange production methods,improve the production efficiency of the enterprises,and improve the comprehensive competitiveness of the enterprises.This thisis conducts a more in-depth study on the workshop scheduling problems,the main research contents are as follows:(1)This thesis conducts a more in-depth systematic research and analysis on the workshop scheduling problem,and determines the characteristics of the workshop scheduling problem,especially the large-scale workshop scheduling problem.The performance evaluation index of the algorithm in solving the problems of workshop scheduling is described.The evaluation system including maximum completion time,maximum delivery time,processing time and delay time is analyzed.(2)This thesis optimizes the inertia weight of the basic particle swarm algorithm,and uses the adaptive nonlinear adjustment strategy to make the algorithm more in line with the characteristics of the problem in the search process,and improve the convergence of the algorithm;in the later stage of algorithm iteration,Gaussian mutation operator is used to increase the diversity of the population,while improving the distribution of the population,so that the local search ability of the algorithm and the global search ability can be effectively balanced.(3)In view of the high complexity and dynamics of the flexible job shop scheduling problem,the sub-population co-evolution and shuffling strategy is proposed to improve the overall optimization ability of the algorithm.This strategy uses local search and shuffling exchange information within each sub-population to ensure the search ability of the algorithm;In terms of iterative rules,this thesis proposes the principles of Hamming distance and solution space locality to improve the efficiency of the algorithm in solving flexible job shop scheduling problems;In order to ensure that the population as a whole iterates in the direction of high adaptability,the roulette algorithm is introduced into the algorithm to further ensure the convergence of the algorithm.