Research On Flexible Job Shop Scheduling Problem Based On Improved Genetic Algorithm

Job shop scheduling is designed to make the process meet the needs of production mainly through the rational allocation of resources in the process of processing.The flexible job shop scheduling problem is more consistent with the real processing environment and more complex than the traditional job shop scheduling problem?Therefore,it has more practical significance and theoretical significance.The main content of the flexible job shop scheduling problem is to determine the processing order of the workpiece and to select the processing machine for the process.Firstly,the research status at home and abroad and the specific application method of genetic algorithm in flexible job shop scheduling are introduced in this paper.Secondly,the genetic algorithm is improved,and it is applied to the flexible job shop scheduling with different optimization targets.The main research work of this paper includes the following aspect:(1)Aiming at minimizing the largest makespan for flexible job shop scheduling problem,an improved multi-population genetic algorithm(IMGA)is proposed in this paper.The lateral and longitudinal evolutionary mechanism is used.The directed evolution is used for genetic algorithm,and directed evolution probability is also designed in order to accelerate the speed for getting the optimal solution.In the selection process,the resurrection strategy is used to avoid losing some good genes by combining eliminated individual genes with outstanding individual genes during the evolving operation.(2)In the actual flexible job shop scheduling problem,not only the work piece requires processing time,but also transferring the work piece by using AGV between the various machines also requires time,therefore,doing research on the flexible job shop scheduling problem with AGV constraint is more practical and significant.Aiming at this problem,the mathematical model of flexible shop scheduling problem with AGV constraint is established.Then,a multi-segment encoding method is proposed,and this can directly eliminate some genes that have no help for evolution process.The adaptive crossover probability,mutation probability and multi-population evolution mechanism are proposed to enhance fast convergence and improve global optimization results.(3)The study of jobshop scheduling focused on makespan,a single machine load and machine total load,but the study of the power consumption of machine and the comfort level of employee is less.Due to the green manufacturing and the comfort level need to care about,the makespan,the power consumption of machine and the comfort level of employee were taken as research objects in this paper.First the method of weighting initialized the population.Second the modified crossover and mutation avoided the illegal solution.Third the elitism strategy retained good genes.And then the method of weighting converted the multiple target into the simple target and the fast decoding gain the fitness value.Finally use test to verify the algorithm.(4)Aiming at the dynamic flexible job shop scheduling problem,a dynamic rescheduling strategy based on variable interval rescheduling is proposed.A mathematical model of dynamic job shop scheduling problem is established.A method to initialize the population is proposed by combining machine initialization,process initialization and random initialization,and therefore the quality of the initial population solution can be made a further improvement.(5)To popularize the research results,a flexible job shop scheduling software which can provide technical support for workshop is developed.