Modeling And Solving Of Dual Resource Constrained Integrated Process Planning And Scheduling

With the increasing pressure on manufacturing industry,it is necessary for a manufacturing enterprise to adopt a multi-variety,small-batch,customer-oriented production mode instead of mass production mode.The customer-oriented production mode requires the flexible manufacturing system.The flexible manufacturing system is composed of process planning and flexible job shop scheduling.In this paper,the integration optimization of process planning and job shop scheduling under the resource constraints of machines and workers is studied and solved.Firstly,based on AND-OR graph and DAG,this paper proposes an AND-OR node graph to show flexibilities in process planning.A model of dual resource constrained Integrated Process Planning and Scheduling with objectives of.minimizaton of makespan,total energy consumption and the variance of work-time of workers.An array representation is developed to describe key flexibilities in the process planning.An integer coding scheme,its initialization mechanism,crossover operator and mutation operator are designed.Combined with the intrusive tumor growth optimization(ITGO)and the third version of the non-dominant sorting genetic algorithm(NSGA3),a multi-objective invasive tumor growth optimization algorithm(MOITGO)is proposed.In MOITGO,cells are classified by the fast non-dominant sorting(FNS)method and the screening method based on reference point which used in NSGA3.In order to prevent too many repetitive cells,a replacement steps is designed.The growth and invasion of cells are redesigned based on the crossover operator and the mutation operator.Combined with the backtracking search algorithm(BSA)and the NSGA3,a multi-objective backtracking algorithm(MOBSA)is proposed.An optimization to historical population is proposed by introducing the weight factor.The mutation step and the crossover step in the BSA are improved with the crossover operator and the mutation operator.The diversity of the population and the convergence speed of the algorithm are improved by adaptive scale of the crossover and the mutation.The FNS and the selection method based on reference points are used to improve the selection of test population.Based on the proposed algorithm,the numerical examples are solved.The effectiveness and superiority of the proposed algorithm are verified by the comparison and analysis with the traditional multi-objective optimization algorithm in the three parameters of the solution set: supervolume,distribution,and extensibility.For the typical cases in the actual production,the multi-objective intrusion tumor optimization algorithm and the multi-objective retrospective search algorithm are used to solve the problem,and the Pareto optimal solution set obtained by the two algorithms is combined to obtain multiple process planning and shop scheduling schemes.