Distributed processing and assembly multistage manufacturing system consist of multiple job shop for processing parts and a general assembly flow shop for partial and final assembly.Dynamic orders with multi-layer product structures require part matching before assembly.The control of such multistage shop involves joint decision-making on order allocation,processing,and assembly jobs scheduling for precise coordination between the two levels of production.An automatic design method of scheduling rules for distributed machining and assembly multistage workshops based on genetic programming based on parallel simulation(GP-PS)was proposed.Firstly,a mathematical model was established to minimize the percentage of tardy product.Then,an improved genetic programming algorithm was introduced to integrate evolutionary multistage scheduling rules.A population optimization mechanism was designed to prevent the algorithm from converging locally,while parallel simulation technology was embedded to effectively reduce training time.Finally,simulation experiments were conducted to validate the performance of the improved genetic algorithm.The experimental results show that the percentage of tardy product obtained from the manual rule set,standard genetic programming,and improved algorithm is respectively 6.44%,5.65%and 2.67%.The improved GP algorithm based on parallel simulation optimization can achieve more obvious comprehensive performance advantages than dozens of preferred artificial rule groups and the optimal rule groups generated by standard GP algorithm.It is feasible and effective to use this algorithm to automatically design multi-level rules of integrated scheduling for DPAMW scheduling problem.
维普
万方数据