Research And Application Of Improved Hybrid Particle Swarm Optimization In Job Shop Scheduling

The competition among enterprises has become extremely fierce with the development of economic globalization,enterprises must pay attention to the scheduling management of production workshops if they want to survive,especially in the manufacturing industry.Reasonably optimizing allocating production resources and increasing production capacity and production efficiency are the most effective means to enhance the market competitiveness of enterprises,so how to solve the problem of workshop scheduling has important theoretical and practical significance.The job shop scheduling problem has become one of the hot topics that scholars at home and abroad have continuously studied and explored in recent years,as a combinatorial optimization problem often encountered by a company in the workshop production management process.With the development of computer technology and the rise of cross-disciplines,intelligent optimization algorithms have gradually become the main method to solve such problems.Particle swarm optimization is an evolutionary search calculation method that finally achieves an optimal state by simulating bird flight and changing its position and speed during the movement,but the application of this algorithm in the field of solving workshop scheduling is not yet mature,and there are some defects.Aiming at the shortcomings of particle swarm optimization,which is easy to fall into local optimal solution and the convergence speed of the algorithm is late,an improved hybrid particle swarm optimization algorithm is proposed in this paper.Firstly,the stochastic inertia weights strategy is added to the standard particle swarm optimization algorithm,so that the algorithm can flexibly adjust the global search and local search ability.At the same time,the crossover and mutation ideas in the genetic algorithm are introduced to increase the diversity of the population and prevent the local optimum solution from being trapped.Finally,the simulated annealing algorithm was combined at the later stage of population evolution,using Simulated Annealing Algorithm's ability to jump out of local optimum to ensure the global optimal solution can be searched when the population evolution becomes stagnant.Finally,a modified hybrid particle swarm optimization algorithm is used to simulate the classical example in the job shop scheduling problem,comparing the experimental results with the experimental results of other algorithms,the effectiveness of the improved algorithm is verified.And through careful study of the actual scheduling process of an enterprise,the improved hybrid particle swarm algorithm is applied to the actual scheduling problem of the enterprise.It realizes the development of a shop scheduling management information system for a company and effectively improves the production management efficiency of the company.