Research On Job-shop Scheduling Problem Based On Particle Swarm Optimization Algorithm

Production scheduling is a hotspot of manufacturing system and the core of the whole advanced manufacturing system to achieve the development of management technology, optimize technology, automation and computer technology. The research and application of effective scheduling method and optimization technology is the foundation and the key to realize advanced manufacturing and improve production efficiency. And algorithm research is one of the important content of the production scheduling problem. In recent years, various intelligent computation methods have been gradually introduced into the scheduling problem, such as genetic algorithm and simulated annealing algorithm, etc.Workshop scheduling is a kind of NP problem. Aimed this problem, the article first systemicly describes the characteristics, research content, evaluation index and development status of workshop scheduling problem, and summarizes the various scheduling problem solving methods in the previous. Secondly, based on systemicly described the basic thoughts of particle swarm optimization algorithm, the basic principle, basic process and performance analysis, the article proposes the research direction of particle swarm optimization, and the simulated annealing algorithm is introduced. Then, the mathematical model of solving job-shop scheduling problem is established, combined with encoding processes, which based on working procedure, uses the improved location update strategy, takes the complete time as optimized object, constructes the solving method based on the standard particle swarm algorithm of production workshop scheduling problem, and verifies the algorithm's convergence and effectiveness by scheduling job-shop benchmark problem. Finally, in view of the standard particle swarm optimization algorithm can not solve the complexity of the production of job-shop scheduling problem, the metropolis sampling criteria is introduced into the PSO algorithm, combined other algorithms with particle swarm optimization algorithm, constructes three kinds of fusion simulated annealing thoughts of hybrid particle swarm algorithm respectively. Comparing the results of hybrid PSO with the other algorithms in scheduling the job-shop benchmarking problem, the effectiveness and superiority of the hybrid Particle Swarm algorithm are verified.