Flexible Job Shop Scheduling Problem Based On Improved Genetic Algorithm

Our country is a manufacturing powerhouse,so its development of manufacturing industry reflects our country’s productivity.Since production shop scheduling is the basis of manufacturing system,its optimization naturally becomes the core of modern manufacturing technology,and also the key technology to realize the production efficiency.However,many distribution box manufacturers have such problems as low efficiency of job shop scheduling management,unstable performance of existing scheduling strategies,being easy to be affected by managers’ experience,and being easy to fall into local optimal solutions.So it is urgent to introduce a more scientific and efficient production scheduling management mode to improve job shop scheduling efficiency and optimize enterprise production management performance,so that it can make positive contributions to reducing cost and increasing efficiency and promoting the comprehensive development of the enterprises.In this paper,on the basis of in-depth study of the domestic and foreign shop scheduling problem and genetic algorithm and other related theories and methods,the algorithm process of genetic algorithm is improved,the variable neighborhood search strategy is introduced,and the distribution box shop scheduling case of a company is simulated.The main contents of the paper are as follows:Firstly,this paper uses genetic algorithm to optimize flexible job shop scheduling problem(FJSP)for discrete production.In the genetic algorithm,each solution is represented by a chromosome.After three genetic operators of selection,crossover and mutation,the chromosome outputs the latest offspring chromosome when the termination condition is triggered,which is the optimized solution.To solve the problem of slow convergence of traditional genetic algorithm,an improved variable neighborhood search hybrid genetic algorithm(HGA-VNS)is proposed in this paper.The improved chromosome consists of two parts.The first part is encoded according to the selection of processing machine,and the second part is encoded according to the processing process.Secondly,in this paper,the improvement of FJSP algorithm is made from two aspects.On the one hand,a combination method of crossover and mutation operators is proposed to improve the genetic operators in the basic genetic algorithm from the perspective of genetic operators.On the Other hand,the disjunctive graph method is used to identify the critical path,determine the key steps on the critical path,and introduce the neighborhood search strategy to improve the algorithm.Finally,based on the constructed algorithm model,the paper uses the HGA-VNS model for data simulation,and introduces the distribution box job shop scheduling of a company for empirical research.The proposed HGA-VNS model is applied to optimize the actual FJSP of the enterprise and compared with the existing scheduling scheme.The final data results show that the completion time of the optimized scheduling scheme is reduced by 26%,and HGA-VNS can obtain a more efficient and economical solution.Therefore,HGA-VNS is effective in solving the problem of flexible machining shop in machining system,and its performance is better than that of the traditional genetic algorithm,obviously superior to the existing production scheduling scheme adopted by a company,so it has certain theoretical research value and practical application value.Figure[35] Table[12]...