Research On Multi-objective Integrated Process Planning And Job Shop Scheduling Under Uncertain Disturbance

Process planning and job scheduling are important components of advanced manufacturing systems.The integration of the two systems is of great significance to improve the performance of manufacturing system.With the development of intelligent manufacturing technology,the integration and data interaction between processes planning and scheduling systems have been enhanced,but uncertain disturbance factors in the workshop restrict the response ability of process planning and scheduling systems.The study of dynamic response methods for integrated process planning and job shop scheduling(IPPS)has an important influence in improving the performance of intelligent manufacturing systems.IPPS problem is one of the most difficult NP complete problems,and the IPPS problem considering disturbance is more complex.At present,the IPPS problem has been widely studied,but the multi-objective IPPS problem considering uncertain perturbations is still lacking.This paper deeply studies the multi-objective IPPS problem under uncertain disturbances,and explores the coping mechanisms and efficient solutions for different types of disturbance factors.1)Firstly,the integration method and solution method of IPPS problem were studied.Based on the NSGAII algorithm and simulated annealing algorithm,a two-stage hybrid algorithm was designed to solve the multi-objective IPPS problem.A process modification strategy was proposed to adjust the process route generated in the process stage to improve the interaction between the two stages and improve the solution performance of the algorithm.Finally,three comparison algorithms were designed to solve 24 groups of classic IPPS problem cases respectively.The results show that the proposed algorithm has advantages in 21 groups of problems,and proves the effectiveness of the proposed algorithm in solving multi-objective IPPS problems.It lays the algorithm foundation for the follow-up research.2)For IPPS problem with uncertain processing time and due date.A fuzzy multi-objective IPPS mathematical model was constructed by using fuzzy numbers to represent the time uncertainty.A multi-layer collaborative optimization algorithm was proposed to solve the problem.The collaborative algorithm includes process planning layer,process selection layer and scheduling layer.In the process planning layer,genetic algorithm is used to generate a better process route for a single job.In the process selection layer,a multi-objective genetic algorithm was designed to generate the best process information of jobs set.In the scheduling layer,based on the MOGA algorithm,the boundary search strategy was added,and the BMOGA algorithm was designed to optimize the final processing plan.Moreover,a shared function strategy was proposed to reduce the chance of individuals producing offspring in areas with high density of individuals and increase the diversity of the population.Due to the characteristics that it is difficult to select the non-dominated solution in multi-objective optimization,an individual comprehensive evaluation strategy was designed,which uses the different ranking of individuals on each objective to assign different scores to evaluate the quality of the non-dominated solution in the population.The results of the two-stage solution method on 32 cases of different scales are compared,and the feasibility and effectiveness of the MLCO algorithm were verified.3)The characteristics of the dynamic IPPS problem considering machine faults were deeply studied.A DIPPS problem model considering rescheduling stability was established.A pre-reaction scheduling method was proposed to deal with the DIPPS problem considering machine fault.In terms of dynamic scheduling strategy,a process adjustment strategy based on jobs classification was proposed to improve the stability of rescheduling.In terms of algorithm,a hybrid algorithm for solving DIPPS problem was designed by combining genetic algorithm and neighborhood search algorithm,and a hierarchical migration strategy of the population was proposed to maintain the diversity in the evolution process of the population.A total of 6 comparison strategies were designed on job shop scheduling,flexible job shop scheduling and IPPS problem.The effectiveness of the proposed PAS strategy was verified on24 groups of test sets of different scales,especially for large-scale IPPS problems.Based on the above research results,an IPPS system under uncertain disturbance was developed.The production management system can realize the functions of user management,process management,equipment management,production management.Finally,the system was applied to the production workshop of a packaging machine,and the results show that the system has great performance.