Research On Group Scheduling In Multi Variety And Small Batch Mode

With the acceleration of the process of industrialization,the mass production mode of traditional manufacturing has been unable to meet the needs of the current market.Now,more products are customized by customers,which makes the production features gradually change into multiple varieties and small batches.This feature also leads to frequent setup activity in the processing,which reduces the production efficiency of the workshop and equipment utilization.Therefore,in view of the increasing number of setup activity problems in the workshop,it is necessary to carry out research on group scheduling under the multi-variety and small-batch mode.Supported by the major science and technology project of Sichuan Province "Key Technology Research and Application Demonstration of Digital Workshop for Complex and Precision Parts of Electronic Product"(Project No.2020ZDZX0025),this thesis took the workshop with multi-variety and small-batch mode as the research object to optimize the scheduling plan during production.The main content was divided into the following parts:Firstly,in view of the problem that only one kind of process path was considered in the previous work-piece Group Technology,the model of work-piece Group Technology under multi process paths was established.The process similarity was selected as the principle of classification,and the average similarity of work-piece was taken as the goal.Genetic Algorithm with improved coding method was used to solve the model,and the algorithm effectiveness was analyzed.The process paths of various electronic products were used as an example to verify the results.Secondly,in order to minimize the maximum completion time,a single machine group scheduling model was established.Depends on the setup time-the shortest processing time first – the improved Ant Colony Optimization was designed to solve the scheduling problem.The initial solution was generated by the rule that the setup time is the least and the shortest processing time is the first,and the improved Ant Colony Optimization was used for optimization.The Taguchi design method was used to optimize the parameters of the improved Ant Colony Optimization,and the effectiveness of the algorithm was verified by simulation.At the same time,processing workpieces of a process in the production of an electronic product were used to verify the effectiveness of a single machine group scheduling method.Finally,because the processing speed of each equipment was different and the processing time and setup time were also different.So,according to the scheduling dependent setup time,the corresponding equipment of each work-piece group was allocated first,and then the scheduling of each work-piece group was optimized.In this regard,to minimize the maximum completion time,a parallel multi machine group scheduling model was established.Depends on the setup time-the shortest processing time first – Genetic Algorithm Simulated Annealing was designed to solve this problem.The initial solution was generated by the rule that the setup time is the least and the shortest processing time is the first,and Genetic Algorithm Simulated Annealing was used for optimization.The Taguchi design method was used to optimize the parameters of the Genetic Algorithm Simulated Annealing.According to the optimal combination of parameters,the effectiveness of the algorithm was verified and an example was analyzed to prove the effectiveness of the parallel multi machine group scheduling model and algorithm.This thesis provided some basic research ideas for the workshop scheduling of manufacturing enterprises with the mode of multi-variety and small-batch,and supplied method support for effectively improving frequent setup activity and enhancing the performance of scheduling algorithms.