Research On Flexible Job Shop Scheduling Problem Based On Genetic Algorithm

With the advent of economic globalization,enterprise becomes more competitive,and shop scheduling is the core of manufacturing enterprises,so improving the production schedule has been considerable attention.Although classical job shop scheduling problem has made a lot of research results,but the gap between classical job shop scheduling problem and actual production is very big,so this study would be a flexible job-shop scheduling problem.So-called flexibility means that workpiece process can select more than one machines,that classical job shop scheduling problem can select machines is single.Due to fierce competition in the market,the enterprise wants to has a position in its field,it need certain reforms for traditional shop scheduling,however flexible job-shop scheduling is more in line with actual production than traditional scheduling production.It increases the number of the machine,but at the same time it can shorten the manufacturing period and reduce the wear on these machines,so the flexible job shop scheduling has become the focus of the enterprise.Genetic algorithm has been widely applied to many scheduling problems,and demonstrated their strong robustness in finding global optimal solution,but it has some shortcomings,that is,local search ability is poor,so in order to cover the shortage,this paper mixes tabu search algorithm,although tabu search algorithm is poor in global search aspect,it has strong local search ability,integration of global search of genetic algorithm and local search of tabu search algorithm is the core of the article.At the same the article has a certain amount of improvement for this algorithm,to improve search efficiency of the algorithm.Finally,in order to prove the validity and rationality of the algorithm,it emulates for the algorithm.The simulation is divided into three parts,the first part is compared with standard genetic algorithm,the simulation result is significantly better than standard genetic algorithm,the second part is compared with algorithms of some scholars,simulation result also overmatch their results,the third part is the algorithm of this paper is applied to actual production,the result shows the algorithm has a certain validity and rationality.This algorithm improves the problems that local search ability of genetic algorithm is poor and the probability of the convergence is low,objective function can meet the requirement of the customer,at the same it has a direct effect on actual production.