Based On Particle Swarm Optimization And Taboo Search Algorithms Solving Job Shop Scheduling Optimization Problems

Along with the science and technology unceasing development, In people’s heart,science and technology change the world. The application of how to make enterprise as soon as possible bring convenience by science and technology, promote the way of enterprise’s production management transformation from extensive to intensive, so as to enhance the competitiveness between the enterprises, reducing the production cost of enterprises, these are all the enterprises facing the serious problem. Enterprise in the practical production, efficient and reasonable scheduling scheme and the advanced optimization techniques has become the key to enterprises to improve production efficiency. Only enterprise to improve its own scheduling scheme can in the increasingly fierce competition. The essence of the workshop scheduling problem is how to allocate the existing resources to meet the normal production enterprise, that is how to deal with the workpiece, the processing of equipment order as well as the relationship between the time, to maximize the use of equipment, improve production efficiency. The study of the workshop scheduling problem has extremely important value of research and production.This paper combines particle swarm optimization algorithm and tabu search algorithm, designed a new algorithm of particle swarm algorithm based on tabu search, we research job shop scheduling problems using the mixed algorithm. Because the dependence of tabu search algorithm’s initial value is bigger, in the beginning,this algorithm will be lack of information, resulting in a loss of the speed of convergence, and the speed is slow, and particle swarm optimization operation simple and easy to realize, global optimization ability is strong, but to slow convergence speed and so on characteristics, therefore the particle swarm algorithm the optimal solution as the initial solution of tabu search algorithm, reduced the dependence of the initial solution of the tabu search for, because of the tabu search algorithm with strong local search ability very good solve the later it is difficult to the convergence ability of particle swarm optimization, so as to speed up the convergence rate, and improve the precision of convergence and improve the performance of the proposed algorithm. Aiming at these problems, this thesis mainly on two particle swarm optimization algorithm and tabu search algorithm for the complementary advantages combination of research, finally put forward and set up a complete set of hybrid algorithm-tabu search of particle swarm optimization algorithm.Finally realized using a tabu search of particle swarm optimization algorithm to solve job-shop scheduling problem of simulation system, using the classic example of job-shop scheduling problem for testing, through compared with the classical examples of the actual analysis, evaluation of the efficiency of the hybrid algorithm, and finally apply the hybrid algorithm to shop scheduling simulation system, the results show that particle swarm of tabu search algorithm has good convergence accuracy and is feasible, and the solution of the higher efficiency, comparing with the traditional scheduling algorithms, embodies the obvious superiority, the simulation results prove the effectiveness of the proposed algorithm.