Research On The Optimization Of Flexible Process Planning And Job Shop Scheduling

Process planning system and job scheduling system are very important subsystems for the intelligent manufacturing system,they have a serious impact on the processing capacity,the resource utilization and the product efficiency of the manufacturing system.With the strengthening of the order individual demand,process planning and job shop scheduling can not integrate effectively,so it can not adapt to the dynamic changes of products demands.Because the process planning and job shop scheduling are mutual restraint,mutual influence,if they are integrated,not only can improve the product efficiency,but also can be a timely response to the in individual needs of the order,making the production planning more flexible.Based on the above considerations,this paper focuses on the integration strategy and algorithm of the two.Firstly,proposed a flexible process planning method based on Artificial Bee Colony algorithm.Focused on the research on encoding and search strategy of the algorithm,designed a sequence encoding to encode for process information.In order to ensure the diversity of the population,proposed the local search mutation operation and global search crossover operation,to complete the search of the solution space,thus improved the performance of optimization algorithm.The efficiency and stability of the algorithm were proved by an example.On the one hand,the research extends the application field of Artificial Bee Colony algorithm,on the other hand,it lays the foundation for solving the optimization problem of integrated flexible process planning and job shop scheduling.Secondly,based on the above research,presented the optimization of single objective integrated flexible process planning and job shop scheduling based on Artificial Bee Colony algorithm.Taking minimum the maximum completion time as a goal,according to the optimization strategy of integrated flexible process planning and job shop scheduling,the artificial bee colony algorithm was designed to solve the problem.The necessity and effectiveness of the algorithm were verified by a numerical example.Finally,designed a multi-objective Artificial Bee Colony algorithm based on Pareto method to solve the problem of integrated flexible process planning and job shop scheduling.In consideration of the maximum completion time and the total processing costs and the total tardiness at the same time,introduced Pareto method,made improvement to the basic Artificial Bee Colony algorithm.The method designed a new fitness evaluation standard and follow the probability formula,proposed Pareto control based on greedy criterion,based on the fast non dominated sorting method to construct Pareto optimal solution set save,the optimal solution set by the external file.Based on crowding distance solution designed the diversity maintenance mechanism.The proposed algorithm can effectively provide the solution of the integrated flexible process planning and job shop scheduling,which provides a decision-making basis for the optimization of production scheduling for the intelligent manufacturing system.