The Research And Application Of Hybrid Quantum Particle Swarm Optimization In Flexible Job Shop Scheduling

In the manufacturing industry,the production scheduling problem is one of the core contents of enterprise production management.The scientific and effective scheduling scheme has a positive effect on the production management and production efficiency of the enterprise.Flexible job shop scheduling is a complex problem in the shop scheduling problem.It is more in line with the actual production situation than the job shop scheduling,It has high computational difficulty.Many basic algorithms are easy to fall into local optimum when optimizing this kind of scheduling problem.Effectively solving the scheduling problem,the optimization of the shortcomings of the algorithm or the mixing with other algorithms to obtain an excellent scheduling solution is getting more and more attention of researchers.In this paper,the hybrid optimization of quantum particle swarm optimization is used to solve the multi-objective optimization problem in flexible job shop scheduling.The specific work is as follows.In the flexible job shop scheduling environment,for multi-objective problem solving,various constraints must be considered when determine important solutions,and multiple targets may also conflict with each other.A relatively good solution is the primary task of multi-objective optimization.So this paper propose a hybrid quantum particle swarm optimization algorithm.In the initialization process,the particle is encoded,and a regular coding method is used to ensure the quality of the initial solution,so that it starts searching in a better direction.Aiming at the weakness of quantum particle group which is easy to fall into local optimum,this paper proposes to use the fast non-dominated sorting in NSGA-II algorithm to find the non-dominated solution,store it in a certain scale of external storage,and then the particles in the external repository.The crowded distance formula is used to calculate the size,and the crowded distance of each particle is compared.The particle with the most crowdedness is selected as the global optimal position,and the purpose is to attract each particle to the non-dominated solution of the sparse region in the external storage.Search,closer to the Pareto solution.The only parameter of the quantum particle swarm optimization algorithm is to choose the cosine decreasing expansion-contraction factor to prevent the late iteration from falling into local optimum.The cross-variation operation in the genetic algorithm is selected by selecting the particles not in the external repository with a certain probability.If the cross-mutated particles dominate the particles in the repository,the cross-mutated particles are replaced by the particles in the repository.On the contrary,the particles in the repository are selected to ensure the superiority of the population.The algorithm of this paper is simulated and tested by the minimum completion time of the classic example,the minimum total machine load and the minimum bottleneck load.Finally,in order to prove the practical significance of the algorithm,through the careful study of the actual scheduling process of a slewing bearing processing workshop,the algorithm is applied to the actual scheduling problem of the enterprise to realize the development of the dispatching management information system for a slewing bearing workshop.It has a good practical application results.