Study On Large-scale Workshop Scheduling Problem In Discrete Manufacturing Enterprise APS

With the development of economic globalization,China's manufacturing industry is facing more severe challenges.The market environment is complex and changing,and demand is diversified.It effectively seizes market opportunities and provides customers with products in a timely and efficient manner as a tool for corporate success.It is inevitable for enterprises to use information to establish accurate production plans and process monitoring.Advanced planning and scheduling(APS)can solve the defects of mainstream information software planning and scheduling,and quickly and accurately develop a global The optimal planning and scheduling logic and algorithm are the main difficulties of the APS system.This article conducts a more in-depth study of this issue.Large-scale discrete manufacturing enterprises have a large scale of production and a large number of products,processes,and equipment.Aiming at the large-scale shop scheduling problem in APS of discrete manufacturing enterprises.this paper adopts the combination of quantum evolutionary algorithm opimizaion and data decomposition.and proposes a quantum evolutionary algorithm based on the target cascade model to solve the large-scale shop scheduling problem model.Improve the efficiency of the algorithm,and can obtain a higher solution quality.Firstly.in view of the large amount of data in large-scale scheduling problems,this paper proposes a data decomposition method based on process similarity clustering.According to the similarity of different workpiece processing technologies,process similarity clustring of different workpieces to obtain corresponding parts According to the principle of process and equipment correspondence,a corresponding equipment distribution model is established.corresponding equipment is assigned to the part family,constitutes a reasonable manufacturing unit,and is used as a basic scheduling model for subsequent quantum evolutionary scheduling algorithms,thereby transforming large-scale workshop scheduling problems.For the production of cell scheduling problems.Secondly,aiming at the problem of large-scale workshop manufacturing cell scheduling,an improved quantum evolutionary algorithm is proposed.The scheduling algorithm,random search,and elitism evolution strategy are added to the basic algorithm.The order of processes is determined by the qubit encoding and the equipment is based on the scheduling rules.Selection,through the iterative evolutionary strategy of the elite,reduces the coding complexity of the scheduling problem and reduces the scope of understanding space.Comparative studies show that the improved quantum evolutionary algorithm can converge to the optimal value faster than other algorithms such as ant colony and genetic algorithm.For different examples and algorithms,the average convergence algebra is increased by 35%-80%,and the optimal solution is increased by 0-26%,running time increased by 3%-91%;when the data size of each dimension increases,the optimal value fluctuation value is not more than 0.08,which proves the superiority of quantum evolution algorithm in solving quality,efficiency and stability.Finally,aiming at the large-scale multi-objective flexible shop scheduling problem,a quantum evolutionary algorithm based on target cascade is designed,and the strategy of emergency inserting and equipment failure in dynamic shop scheduling is designed and solved by examples.The example calculations show that the efficiency of the algorithm is greatly improved,which verifies the feasibility of the quantum evolutionary scheduling algorithm in the goal cascade framework of this paper.