Shop Scheduling Optimization For Discrete Manufacturing Industry

As a key problem in production management,shop scheduling determines the allocation of processing elements in an enterprise,which is very important for discrete manufacturing enterprises.However,due to the complexity of the shop scheduling problem,it is difficult to obtain a high-quality scheduling scheme by using the traditional optimization methods,which makes most of the discrete manufacturing enterprises use the manual method for shop scheduling.This method not only has high cost and low efficiency,but also lacks quality assurance and cannot cope with largescale production.To solve the shop scheduling problem in discrete manufacturing industry,firstly,three optimization objectives(minimizing the maximum completion time,minimizing the transportation time of intermediate products,and minimizing the degree of load imbalance among machines)are determined,and the corresponding shop scheduling model is established.Then,an improved genetic algorithm is designed and implemented to solve the shop scheduling model.In the improved genetic algorithm,the crowding distance,the set of excellent individuals which is independent of the population and the deterministic selection strategy without replacement are used to maintain the diversity of the population,and the local search module is used to perform neighborhood search for the excellent individuals in each generation.The results of comparative experiments and application verification show that the improved genetic algorithm can provide a lot of high-quality scheduling schemes for decision makers and efficiently solve the multi-objective shop scheduling problem.Using the improved genetic algorithm to solve the shop scheduling problem can significantly shorten the manufacturing cycle of enterprises and improve the resource utilization rate and economic benefits of enterprises.