Research On Lot Streaming In Parallel Machines Scheduling With Mold Constraint

Manufacturing industry is a pillar industry of the national economy.Under the background of ―Industry 4.0‖ and ―Made in China 2025‖,most manufacturing industries is turning to intelligent manufacturing and smart factory.For manufacturing plants,the level of automation and informatization is getting higher and higher.Under such a production environment,it is particularly important to realize practical production scheduling.Reasonable production scheduling can improve production efficiency,reduce costs,shorten production cycle and realize JIT.Based on the ever-changing market environment and the increasing individualization of customer needs,the degree of flexible manufacturing in manufacturing enterprise has been greatly improved.During the manufacturing process,there will be multiple parallel devices with similar functions.At the same time,the same product can be processed in parallel.This type of problem belongs to the parallel machine scheduling problem.However,Most of the literatures concerning the parallel machine scheduling optimization model assume that the product is produced individually or in batches,mold constraints and sequence-dependent setup times are not considered,and less research on multi-objective problems.In response to these problems,this paper studies how to split the job and make a reasonable scheduling scheme when considering the mold constraint in the background of an auto parts enterprise.The important research results are as follows:(1)Based on the traditional parallel machines scheduling problem model,considering the mold constraints and sequence-dependent setup times,the optimized objective model of lot streaming in parallel machine scheduling was built for minimizing the makespan.The sub-job number of the product is set as an indefinite variable.For this problem,an intelligent optimization algorithm is designed to solve it.Compared with the traditional parallel machine scheduling model,the model established in this paper considers more practical factors and has higher application value to solve practical problems.(2)Considering that the model has both the sub-job number determination and the sub-job allocation and sorting problem,combined with the GA and DE algorithm to design the HDEGA hybrid algorithm with local search algorithm to achieve the two issues of job splitting and sub-job scheduling in parallel optimization solution.For different size numerical experiments,with the goal of minimizing the makespan,we use the HDEGA algorithm and the GA algorithm to solve it.The results show that the HDEGA algorithm performs well in seeking the stability and convergence of the optimal solution,and verifies the effectiveness and superiority of the HDEGA algorithm.(3)Applying the non-dominated solution sorting algorithm in the NSGA-2 algorithm to the DEGA algorithm.Using the DEGA algorithm for multi-objective to minimize the makespan and the earliness/tardiness penalties for the actual case problem.And the results proves that the DEGA algorithm has practicality and feasibility for solving multi-objective optimization problems of the actual case.