The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies ha...The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.展开更多
This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It cons...This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.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 National Key R&D Program of China (Grant No. 2019YFB1704603)National Natural Science Foundation of China (Grant Nos. U21B2029 and 51825502)。
文摘The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.
基金supported by the National Natural Science Fund for Distinguished Young Scholars of China(No.61525304)the National Natural Science Foundation of China(No.61873328)。
文摘This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.
