The Study Of The Distributed And Flexible Job-shop Scheduling Problem Based On Improved Geneticalgorithms

Distributed and flexible job-shop scheduling problem is applied to shop typeswhat has many varibles,small bath production mode and accorded with the trend ofglobalization in a21stcentury.It can solve the scheduling problem which in thefactories in different places glocally. An improved “king strategy” for selectingcrossover individuals is propsed.The method of improvement is selecting offspringbased on father and son competition machanison.Then these algorithms were operatedin Matlab.The fine scheduling method can optimize the optimal solution of theproduction system and improve economic efficiency.First of all，The study of distributed and flexible job-shop scheduling problem、flexible scheduling problems and distributed scheduling problems is summarized inthe paper. Secondly,the background and the significance of distributed and flexiblejob-shop scheduling problems is pointed out in the paper. Finally,the paper describesthe content and the thinking of the research.Secondly, companied with the difficulties and the characteristics of distributedshop scheduling problem and flexible shop scheduling problem,the overview and thediffculty of distributed and flexible job-shop scheduling problem is researched, and akind of the mathematical model of distributed and flexible job-shop schedulingproblem is established.Thirdly,for solving distributed and flexible job-shop scheduling problem,in thesingle job shop scheduling, a method named an improved “king strategy” for selectingcrossover individuals is propsed in this paper.The method of improvement isselecting offspring based on father and son competition machanison.Then thesealgorithms were operated in Matlab to prove the method of selecting crossoverindividuals is a better way.Fourthly,according to distributed and flexible job-shop scheduling problem,akind of improved genetic algorithm based on double selection and multi-mutation isproposed.First,selection and mutation improve the the diversity of population and thepopulation performance.Then,selection,mutation， crossover and mutation is usedagain.A method named an improved “king strategy” for selecting crossover individuals is used when crossing.Next,the algorirhm compares with other geneticalgorithm in distributed and flexible job-shop scheduling problem to prove thisalgorithm of optimization ability and feasibility.Finally, in the entire production,the shop contacts with each other and isrestricted with each other.so the effect of solving the production planning andscheduling optimization in a single shop is very limited in the entire productionoptimization. If optimizing the entire production planning and schedulingsimultaneously, its calculation work is far from the current technology can solve.Themethod which is proposed in the paper could be realized in a considerable number ofenterprises. Translating the all shop production planning and scheduling integrationoptimization into each shop production planning and scheduling integrationoptimization what can simplify computing processing and workload of theproblem and can deal with the production task orders and the events of changingproduction equipments. Praction proves the method feasible and effective.