基于改进NSGA-Ⅲ算法的多目标柔性作业车间调度

Multi-objective Flexible Job Shop Scheduling Based on Improved NSGA-ⅢAlgorithm

针对多目标柔性作业车间调度问题求解效率低的难题,提出了一种改进NSGA-Ⅲ(non-dominated sorting genetic algorithm-Ⅲ)调度优化算法。首先,建立了考虑直接能耗和间接能耗的多目标柔性作业车间调度模型;然后,结合两段式编码设计了一种混合分配策略,应用于种群的初始化,并通过进化算子确定子代种群的生成;最后,基于参考点的小生境选择策略,利用双层正交边界交叉方法生成一组预定的参考点,并根据种群熵值变化率设计自适应淘汰策略用于非支配精英存储策略。通过对11个作业车间调度问题算例进行改造,验证了改进算法求解多目标柔性作业车间调度问题具有较高的求解质量和求解效率。

Aiming at the problem of low efficiency in solving the multi-objective flexible job shop scheduling problem,an improved NSGA-Ⅲ scheduling optimization algorithm is proposed.Firstly,a multi-objective flexible job shop scheduling model considering direct and indirect energy consumption is established.Then,a hybrid allocation strategy is designed by combining the two-stage coding,which is applied to initialize the population,and the generation of descendant population is determined by evolutionary operator.Finally,based on the niche selection strategy of reference points,a set of predetermined reference points are generated by using the double-layer orthogonal boundary crossing method,and an adaptive elimination strategy is designed for elite strategy according to the rate of population entropy change.Through the reconstruction of 11 job shop scheduling problems,the improved algorithm is proved to have higher quality and efficiency as solving the multi-objective flexible job shop scheduling problems.