In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop pro...In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.展开更多
充分考虑实际生产过程中的批量生产形式,构建以最小最大完工时间为目标的置换流水车间分批调度问题的数学模型,并提出一种改进的人工兔优化算法。在编码阶段,采用最小位置值(smallest position value,SPV)规则实现连续解向离散解的转变...充分考虑实际生产过程中的批量生产形式,构建以最小最大完工时间为目标的置换流水车间分批调度问题的数学模型,并提出一种改进的人工兔优化算法。在编码阶段,采用最小位置值(smallest position value,SPV)规则实现连续解向离散解的转变;在解码阶段,采用动态策略对工件进行分批;通过NEH启发式规则改善初始种群的质量;引入差分进化算子提高解的多样性;提出基于交换和逆序的局部搜索策略增强算法跳出局部最优解的能力。将所提算法和其他对比算法对不同规模的算例进行求解,通过消融实验、对比实验、统计检验等证明了算法的有效性。最后对某汽车外饰件厂喷涂车间排产问题进行求解,求解结果优于其他对比算法,进一步证明了所提算法的有效性。展开更多
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,BPRHFSP)模型,提出一种改进的多目标蜉蝣算法(multi-obj...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,BPRHFSP)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。展开更多
基金supported by the National Key R&D Plan(2020YFB1712902)the National Natural Science Foundation of China(52075036).
文摘In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.
文摘充分考虑实际生产过程中的批量生产形式,构建以最小最大完工时间为目标的置换流水车间分批调度问题的数学模型,并提出一种改进的人工兔优化算法。在编码阶段,采用最小位置值(smallest position value,SPV)规则实现连续解向离散解的转变;在解码阶段,采用动态策略对工件进行分批;通过NEH启发式规则改善初始种群的质量;引入差分进化算子提高解的多样性;提出基于交换和逆序的局部搜索策略增强算法跳出局部最优解的能力。将所提算法和其他对比算法对不同规模的算例进行求解,通过消融实验、对比实验、统计检验等证明了算法的有效性。最后对某汽车外饰件厂喷涂车间排产问题进行求解,求解结果优于其他对比算法,进一步证明了所提算法的有效性。
