Research On Collaborative Optimization Of Dynamic Distributed Manufacturing Production Planning And Scheduling

With the continuous progress of science and technology and the fierce development of market competition,advanced manufacturing system not only meets the market demand from the aspects of production mode,manufacturing cost and work efficiency,but also pursues perfection from the operation mode,and meets the needs of modern production with the best resource utilization and lean production mode.The distributed multi-job shop production mode emerges as the times require.Based on the mutual sharing of information technology and network technology,geographical constraints are broken by distributed job shops,which stand out in the field of manufacturing and gradually become the mainstream mode of production.Therefore,this thesis focuses on the collaborative optimization of production planning and scheduling in distributed job shop.In this thesis,the research status of job shop production planning and scheduling domestic and aborad is summarized first from job shop and distributed job shop.On this basis,the characteristics and operation mode of distributed job shop are analyzed.Aiming at the master-slave relationship of distributed job shops,a multi-job shop competitive and collaborative production planning and scheduling system operation mechanism is proposed,and the production planning of distributed job shop is discussed.Closed-loop integration strategy and response strategy based on disturbance events in dynamic mode are proposed with the optimization process of scheduling.In order to effectively solve the problem of production planning and scheduling in distributed job shop,an optimization model suitable for single-shop production planning and scheduling is established,and a hybrid genetic algorithm integrating chaotic mechanism is designed.The chaotic principle is introduced into the iterative renewal process of local search to ensure the optimal search process.Finally,the production planning in single-shop is realized through neighborhood search.And scheduling optimization.The benchmark data are selected to simulate,and the results are compared with those of other algorithms.The results show that the hybrid genetic algorithm with integrated chaos mechanism has good performance.In order to further study the collaborative optimization problem of master-slave relationship distributed job shop production planning and scheduling,a collaborative optimization model of distributed workshop production planning and scheduling is established according to the characteristics and operation mode of the workshop.A hybrid genetic algorithm based on dynamic balance is designed by analyzing and designing,and the dynamic balance adjustment is realized by using phase updating rules and strategies.At the same time,orthogonal experiments are designed to analyze the influence of some parameters on the performance of the algorithm,so as to determine the optimal combination of parameters.Finally,the performance of the algorithm is verified by simulation.The results show that the optimal combination of parameters can improve the performance of the algorithm.The dynamic balance adjustment strategy in the algorithm can effectively deal with the cooperative optimization problem of production planning and scheduling in distributed job shop under disturbance events.