Application Research Of Hybrid Particle Swarm Algorithm In Flexible Job Shop Scheduling Problem

Manufacturing plays a decisive role in the economic and social development of a country and even the entire world.In recent years,with the changes in the international situation and the surrounding military situation,the aerospace manufacturing industry is facing enormous challenges.As the core technology of aerospace advanced manufacturing technology and management technology,the optimization of job-shop production scheduling will greatly affect the manufacturing cost and efficiency.And scheduling plays an active role in promoting advanced aerospace manufacturing.Thus,the research on scheduling optimization of flexible job-shop problem has extremely important practical significance and application value.In this paper,the research is focused on flexible job-shop problem.And the solution is based on the particle swarm optimization algorithm to further investigate the scheduling problem.A discrete coding scheme is proposed,which consists of an operation sequence vector and a machine allocation vector to jointly represent a particle.A fitness function with left shift operator is designed to improve the efficiency of the algorithm.The evolution of particle swarm is improved.By moving the operator in three ways,the diversity of particle swarm is guaranteed,and the co-evolution between operation sequence vector and machine allocation vector is also satisfied.In order to enhance the local search ability of particle swarm optimization,particle swarm optimization is combined with Tabu search to propose a hybrid particle swarm optimization algorithm.A new mechanism of judging population stagnation is proposed in order to realize the PSO and Tabu search algorithms are combined effectively.In addition,this paper proposes a new domain structure based on critical path,which improves the efficiency of the algorithm.Finally,the simulation results show that the hybrid particle swarm optimization algorithm is efficient.