Research On Job Shop Scheduling Problem With Multiple Process Routes And Lot Split

For intense competition of the market and diversified demands of customer, the production mode with variety and small batch has become the main features of manufacturing in workshop. Job shop scheduling (JSP) is one of the key activities for many enterprises, and it can improve the resource utilization and overall quality of products, and then reduce production costs. In actual job shop scheduling problem, it has important theoretical and practical value to optimize process routes and scheduling. Job shop scheduling problem with multiple process routes and lot split is more complex with larger space, and it is studied in this thesis.Firstly, JSP with multiple process routes and lot split is introduced, and its developments and applications are summarized. Secondly, Job shop scheduling problem with multiple process routes is researched, and a general particle swarm optimization algorithm (GPSO) is proposed with the corresponding mathematical integration model built. In GPSO, crossover and mutation operations in genetic algorithm are respectively utilized by particles to exchange information and search randomly. Besides, Tabu Search (TS) is used for particles'local search. The experimental results show that the proposed GPSO is feasible for JSP with multiple process routes.Thirdly, lot scheduling with multiple process routes in job shop is investigated. Based on GPSO for this scheduling problem, a novel initialization method is used for particle encoding rationally. Then a new crossover operation is proposed for the problem. The computation results show that the improved GPSO is feasible and effective.Next, based on GPSO, a scheduling system is developed, and simulation results indicate the satisfactory performance of the proposed algorithm.Finally, the research in the dissertation is summarized, and the further research direction is discussed.