针对可重入制造系统多具有多品种、大规模、混流生产等特点,构建带批处理机的可重入混合流水车间调度问题(reentrant hybrid flow shop scheduling problem with batch processors,BP-RHFSP)模型,提出一种改进的多目标蜉蝣算法(multi-objective mayfly algorithm,MOMA)进行求解.提出了单件加工阶段和批处理阶段的解码规则;设计了基于Logistic混沌映射的反向学习初始化策略、改进的蜉蝣交配和变异策略,提高了算法初始解的质量和局部搜索能力;根据编码规则设计了基于变邻域下降搜索的蜉蝣运动策略,优化了种群方向.通过对不同规模大量测试算例的仿真实验,验证了MOMA相比传统算法求解BP-RHFSP更具有效性和优越性.所提出的模型能够反映生产的基础特征,达到减少最大完工时间、机器负载和碳排放的目的.
Reentrant Hybrid Flow Shop Scheduling Problem Based on MOMA
For the characteristics of multi-variety,large-scale and mixed-flow production of reentrant manufacturing systems,the reentrant hybrid flow shop scheduling problem with batch processors(BP-RHFSP)is constructed,and an improved multi-objective mayfly algorithm(MOMA)is proposed for BP-RHFSP.Firstly,decoding rules for single-piece processing stage and batch-processing stage are proposed.Then,a reverse learning initialization strategy based on logistic chaotic mapping is designed to improve the quality of the initial solution of the algorithm,also an improved mayfly mating strategy is designed to improve the local search ability of MOMA.Finally,a VND-based mayfly movement strategy is designed based on the coding rule to ensure the quality of the population evolves in a good direction.Through the simulation experiments of a large number of test studies of different scales,it is verified that MOMA is more effective and superior than the traditional algorithm in solving BP-RHFSP.The proposed model can reflect the basic characteristics of production,reducing the makespan,machine load,and carbon emissions.
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