The Research And Application Of Multiobjective Hybrid Optimization Algorithm In Flexible Job-shop Scheduling

The development of science and technology has given the modern manufacturing industry a connotation of the times,which provides a new opportunity and challenge for the development of the modern manufacturing industry.As an important part of workshop production management in manufacturing industry,the development of job-shop scheduling theory also need to be more intelligent and efficient.Of these,flexible job-shop scheduling problem have become the focus of oversea and domestic researchers.To explore more effective intelligent algorithm is not only significant for solving the flexible job-shop scheduling problem,but also has more crucial practical significance.In this academic dissertation,multiobjective hybrid optimization algorithm is adopted to solve flexible job-shop scheduling problem.Firstly,this academic dissertation studies the genetic algorithm in the application of the single objective flexible job-shop scheduling problem.In view of the low efficiency of traditional genetic algorithm,such as slow convergence and local convergence insufficiency,this academic dissertation modified the basic operations of classical genetic algorithm and proposed an improved genetic algorithm using opposition-based learning for the problem.Two improved crossover,multi-parent precedence operation and opposite critical-machine,and two improved mutation,modified neighbor search and opposite inverse,are proposed to improves the basic genetic operation of classical genetic algorithm.Simulation results show that the performance of improved genetic algorithm increase obviously in two aspects of problem solving efficiency and convergence speed,and effectively overcome the shortcomings of the algorithm which easy to converge to local optimal solution.Then,this academic dissertation studies the multi-objective hybrid optimization method in the application of multi-objective flexible job shop scheduling problem.The hybrid algorithm is composed of the improved genetic algorithm of this academic dissertation and multiobjective evolutionary algorithm based on decomposition.The algorithm using tchebycheff method to aggregate multiple optimization objective,then divied individuals into several groups according to the euclidean metric between them,and the improved genetic algorithm of this academic dissertation is adopted in genetic evolution process for each group.Simulation results show that the solving efficiency of problem by the multi-objective hybrid optimization algorithm is good.Lastly,this academic dissertation simulates the problem in the system of flexible job-shop scheduling management.Through the simulation of the process of job and their operations and the whole scheduling process,achieve the simulation of flexible job-shop scheduling problem,and proved the effectiveness of the algorithm in solving practical problems.