Research On Multi-flexible Job-shop Scheduling Problem Based On Improved Genetic Algorithm

In the context of economic globalization and ever-changing science and technology,market competition is becoming increasingly fierce.Manufacturing companies need to optimize the allocation of production factors and improve resource utilization under a reasonable and effective scheduling plan,so as to obtain higher economic benefits.As an important research topic in the field of intelligent manufacturing,flexible job-shop scheduling is of great significance for the scientific and efficient allocation of production resources.Therefore,in recent years,it has attracted the attention of many scientific researchers,and many scientific,efficient and feasible flexible job-shop scheduling schemes have emerged.Because of its simplicity,versatility,and strong robustness,genetic algorithms are widely used in solving various optimization problems,especially in solving flexible job-shop scheduling problems.In this thesis,the genetic algorithm is deeply studied and further improved in solving the problem of multi-flexible job shop scheduling.Experimental results prove that the algorithm proposed in this paper can better solve the problem of multi-flexible job shop scheduling.The main work is as follows:(1)The concept of flexible resources in the multi-flexible job-shop problem is sorted out,problem classification and common research methods are introduced,and the flexible job-shop scheduling problem is described mathematically,and a scheduling model is established.(2)Aiming at the problem of low search efficiency and weak local search ability of genetic algorithm in solving multi-flexible job-shop scheduling,an improved genetic algorithm with self-adjusting search domain method is proposed.The optimization goal of the algorithm is to minimize the delivery time,and a new population initialization method is proposed based on a two-layer chromosome coding scheme based on process arrangement and machine selection.The operation methods and procedures such as selection crossover mutation are introduced in detail,and the scheme for the population to jump out of the local optimum is given.Experimental results show that the improved genetic algorithm improves the optimization accuracy and convergence ability.(3)A fusion co-evolutionary genetic algorithm was designed to solve the problem of premature maturity caused by the lack of interaction between populations.A model of independent evolution of various populations in the early stage of evolution and co-evolution of fusion in the later stage was constructed,and the ability of population evolution was improved by introducing the mechanism of co-evolution into the genetic algorithm.At the same time,aiming at the insufficient consideration of genetic parameter setting,a cross-mutation operation based on adaptive differential evolution is studied.The experimental results show that the co-evolutionary genetic algorithm can effectively solve the multi-flexible shop-shop scheduling problem.