Improved Genetic Algorithm For Solving The Flow Shop Scheduling Problem

In modern society,with the rapid development of science and technology,the commodity manufacturing industry in China will be confronted with huge business opportunities and fierce challenges.Enterprises not only have the ability to respond to the market quickly,but also meet the individual needs of the customers.Reasonable and effective production scheduling can reduce the cost of production and optimize the allocation of resources in the workshop,which can make the enterprises be in an undefeated position under the background of global competition.In recent years,experts and scholars have devoted their efforts to the study of job shop scheduling problems,and have obtained rich theoretical results.But when the model is built,the model is constrained and simplified into a single objective classic job shop scheduling problem,which has a large gap with the actual problem.It is difficult to effectively apply the actual production shop scheduling.Combined with the above analysis,this paper will study the problems faced in the actual production scheduling,including the characteristics of multi objectives.Based on the establishment of a flow shop scheduling model.In this paper,an adaptive genetic algorithm with improved crossover operator and mutation operator will be used to make it change with the change of fitness function.It not only improves the efficiency of the algorithm,but also solves the shortcomings of traditional genetic algorithm,such as easy to fall into local optimum and slow convergence.Using the coding mechanism based on process coding and machine coding,the nonlinear sorting wheel roulette selection operation is adopted in the selection of genetic operators.This paper uses two kinds of cross operation.Process sequencing chromosomes adopt an improved IPOX crossover,which not only inherits the superior characteristics from the parent,but also ensures that all the FSPring produced are legitimate.An adaptive genetic algorithm with elitism preserving strategy is applied to the scheduling problem.The speed of convergence is accelerated and the local optimum is prevented.Finally,the algorithm proposed in this paper is verified by an example and compared with the literature algorithm.The experimental results show that the algorithm proposed in this paper has obvious advantages both in the scheduling result and in the convergence speed of the algorithm.At last,taking the mechanical production workshop of a heavy industry company as the research object,the research work of the flow shop scheduling problem is carried out,and a set of scheduling system which can be applied to the mechanical production workshop of the company is designed.The improved algorithm is applied to the simulation system.Through the processing of the company’s data,better results are obtained and compared with previous algorithms to verify the effectiveness of the algorithm in actual production.