求解柔性作业车间动态调度问题的混合鲸鱼优化算法OA

Aiming at the multi-objective flexible job shop scheduling problem,a multi-objective flexible job shop dynamic scheduling problem model is established by taking the maximum completion time,maximum machine load and total machine load as optimization objectives,and considering the arrival and departure of jobs,machine breakdown and recovery dynamic factors in the actual shop scheduling process.Firstly,the continuous whale optimization algorithm is applied to discrete job-shop scheduling prob-lem by using two-stage coding,the nonlinear convergence factor is used to improve the whale optimization algorithm to balance the exploration and exploitation.Secondly,with the whale optimization algorithm as the core,the Opposition-based learning is used to compare the fitness of the original solution and the opposite solution,and the solution with high fitness is selected to replace the orig-inal solution,so as to prevent the algorithm from falling into the local minimum.The variable neighborhood search algorithm based on the critical path is introduced to search the neighborhood solution better than the current whale individual,and improve the local search ability of the population.Then,combining the improved whale optimization algorithm with the multi-objective framework,a hybrid multi-objective whale optimization algorithm and dynamic scheduling strategy are proposed to solve the multi-objective flexi-ble job shop dynamic scheduling problem model.Finally,experimental results show that the mathematical model and scheduling al-gorithm proposed in this paper can effectively solve the multi-objective flexible job shop dynamic scheduling problem.