Research On Multi-objective Flexible Job-shop Scheduling Problem Based On Genetic Algorithm

The manufacturing industry is the foundation of the country and the foundation of a strong country,which fundamentally determines the comprehensive strength and international competitiveness of a country.The changes in manufacturing reflect the potential and momentum of China!s economy.From manufacturing powers to manufacturing powers,our country is in the midst of progress.Flexible factory scheduling,as a key production process in the manufacturing industry,how to effectively achieve the scheduling goals has always been a hot spot in the manufacturing industry.This paper presents a plant scheduling solution method based on genetic algorithm,focusing on multi-objective solution of shop scheduling,dealing with machine faults and rescheduling after new task insertion.Firstly,based on the complexity of shop scheduling,this paper compares the application of traditional scheduling algorithms and intelligent algorithms,and selects genetic algorithms to solve the problem of shop scheduling.Secondly the application of genetic algorithm in job shop scheduling solves the premature problem,md analyzes its search speed,convergence effect and optimal solution,and presents a new hybrid genetic algorithm.The real population is coded for the initial population,the individuals in the solution space are sorted according to the distance,the roulette selection method is applied,the improved crossover and mutation operators are used,and the simulated annealing algorithm is combined to introduce each generation of genetic evolution.Local search improves the global optimization ability of the algorithm.Finally,in order to solve the problem of machine failure and new processing task insertion in the workshop production process,a rescheduling strategy was designed to deal with such emergencies.Through simulation experiments,the results show that in the case of machine failure and new processing tasks inserted,the given algorithm can still meet the scheduling target and improve equipment utilization efficiency.