新兴信息技术的飞速发展与全球经济一体化格局加速国际强国间新一轮的激烈角逐,智能制造成为我国应对瞬息万变的市场需求,向数字化、自动化、智能化、低碳化转型升级的重大战略决策。柔性车间作业调度是智能制造车间生产计划管理的重要...新兴信息技术的飞速发展与全球经济一体化格局加速国际强国间新一轮的激烈角逐,智能制造成为我国应对瞬息万变的市场需求,向数字化、自动化、智能化、低碳化转型升级的重大战略决策。柔性车间作业调度是智能制造车间生产计划管理的重要问题之一,因其NP-hard本质而备受关注,此外实际运营环境存在的不确定因素使现有解决方案面临巨大的挑战。文章以最小化最大完工时间、最小化误工–提前成本、最小化机器总负载为优化目标,结合作业加工时间随机变化的特性,围绕多目标随机加工时间的柔性车间作业调度问题展开研究,提出一种改进NSGAII的多目标随机加工时间柔性车间作业调度方法(N-MOSFJ)。该方法依据各目标偏好设计种群初始化方法,提升初始解质量和多样性,同时,通过构造参考下界在具有随机加工时间动态变化的解空间中为算法迭代引导可参考的搜索方向。为深入挖掘局部最优解,生成感知绩效因子,并设置动态定位因子以加速全局靶向探索,基于此建立个体综合适应度值评估机制确保算法客观量化个体质量。此外,采用差异优先与质量优先混合选择模式,协同质量约束的“优生多生”交叉变异策略,有效平衡多目标间冲突。通过在仿真数据集和领域公开数据集上对算法进行比较验证,实验结果表明提出的算法具有较好的有效性与稳定性。The rapid development of emerging information technologies and the acceleration of global economic integration have intensified competition among international powers. Intelligent manufacturing has become a critical strategy for China to address dynamic market demands and achieve transformation toward digitization, automation, intelligence, and low-carbon development. Flexible job shop scheduling is a vital problem in production planning within intelligent manufacturing systems. Its NP-hard nature and the uncertainties in real-world operational environments present significant challenges to existing solutions. This study investigates a multi-objective flexible job shop scheduling problem with stochastic processing times. The objectives are to minimize the makespan, tardiness-early cost, and total machine load. A novel method based on an improved NSGAII, called N-MOSFJ, is proposed. The method designs a preference-based population initialization strategy to enhance the quality and diversity of initial solutions. Reference lower bounds are constructed to guide the algorithm’s iterations in a dynamically changing solution space with stochastic processing times. A performance-aware factor is introduced to improve local exploitation, while a dynamic positioning factor accelerates global exploration. An integrated fitness evaluation mechanism ensures an objective assessment of individual solution quality. To balance conflicts among multiple objectives, a hybrid selection strategy combining diversity and quality priorities is adopted. This is integrated with a quality-constrained “elite reproduction and multiple reproduction” crossover and mutation strategy. By comparing and validating the algorithm on simulation datasets and publicly available domain datasets, experimental results show that the proposed algorithm demonstrates good effectiveness and stability.展开更多
针对多目标集成工艺规划与车间调度(Integrated Process Planning and Scheduling,IPPS)问题,建立了考虑完工时间、机器负载、总流程时间和机器利用率四个优化目标的IPPS问题模型。基于模拟退火算法和NSGAII算法提出了一种两阶段的混合...针对多目标集成工艺规划与车间调度(Integrated Process Planning and Scheduling,IPPS)问题,建立了考虑完工时间、机器负载、总流程时间和机器利用率四个优化目标的IPPS问题模型。基于模拟退火算法和NSGAII算法提出了一种两阶段的混合算法求解多目标IPPS问题。工艺规划阶段以最小化加工时间和机器负载为优化目标生成工件工艺路线,调度阶段以最小化完工时间、总流程时间和最大化机器利用率为优化目标生成调度方案,两个阶段交替迭代,完成问题求解。提出了一种工艺修正策略,对工艺阶段产生的工艺路线进行调整,来提高两个系统间的交互能力,从而提高算法的求解性能。最后设计了对比实验,用三种算法分别求解24组经典的IPPS问题案例。结果表明提出的混合算法和工艺修正策略在寻优能力和解的质量上都优于NSGAII算法,验证了提出的算法解决多目标IPPS问题的有效性。展开更多
Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quali...Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quality in the power distribution system.Researchers have considered the use of distributed generation resources in recent years.There are numerous advantages to utilizing these resources,the most significant of which are the reduction of network losses and enhancement of voltage stability.Non-dominated Sorting Genetic Algorithm II(NSGA-II),Multi-Objective Particle Swarm Optimization(MOPSO),and Intersect Mutation Differential Evolution(IMDE)algorithms are used in this paper to perform optimal reconfiguration,simultaneous location,and capacity determination of distributed generation resources and capacitor banks.Three scenarios were used to replicate the studies.The reconfiguration of the switches,as well as the location and determination of the capacitor bank’s optimal capacity,were investigated in this scenario.However,in the third scenario,reconfiguration,and determining the location and capacity of the Distributed Generation(DG)resources and capacitor banks have been carried out simultaneously.Finally,the simulation results of these three algorithms are compared.The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios.Specifically,the energy losses were reduced from 211 to 51.35 kW(a 75.66%reduction),119.13 kW(a 43.54%reduction),and 23.13 kW(an 89.04%reduction),while the voltage stability index(VSI)decreased from 6.96 to 2.105,1.239,and 1.257,respectively,demonstrating significant improvement in the voltage profile.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
文摘新兴信息技术的飞速发展与全球经济一体化格局加速国际强国间新一轮的激烈角逐,智能制造成为我国应对瞬息万变的市场需求,向数字化、自动化、智能化、低碳化转型升级的重大战略决策。柔性车间作业调度是智能制造车间生产计划管理的重要问题之一,因其NP-hard本质而备受关注,此外实际运营环境存在的不确定因素使现有解决方案面临巨大的挑战。文章以最小化最大完工时间、最小化误工–提前成本、最小化机器总负载为优化目标,结合作业加工时间随机变化的特性,围绕多目标随机加工时间的柔性车间作业调度问题展开研究,提出一种改进NSGAII的多目标随机加工时间柔性车间作业调度方法(N-MOSFJ)。该方法依据各目标偏好设计种群初始化方法,提升初始解质量和多样性,同时,通过构造参考下界在具有随机加工时间动态变化的解空间中为算法迭代引导可参考的搜索方向。为深入挖掘局部最优解,生成感知绩效因子,并设置动态定位因子以加速全局靶向探索,基于此建立个体综合适应度值评估机制确保算法客观量化个体质量。此外,采用差异优先与质量优先混合选择模式,协同质量约束的“优生多生”交叉变异策略,有效平衡多目标间冲突。通过在仿真数据集和领域公开数据集上对算法进行比较验证,实验结果表明提出的算法具有较好的有效性与稳定性。The rapid development of emerging information technologies and the acceleration of global economic integration have intensified competition among international powers. Intelligent manufacturing has become a critical strategy for China to address dynamic market demands and achieve transformation toward digitization, automation, intelligence, and low-carbon development. Flexible job shop scheduling is a vital problem in production planning within intelligent manufacturing systems. Its NP-hard nature and the uncertainties in real-world operational environments present significant challenges to existing solutions. This study investigates a multi-objective flexible job shop scheduling problem with stochastic processing times. The objectives are to minimize the makespan, tardiness-early cost, and total machine load. A novel method based on an improved NSGAII, called N-MOSFJ, is proposed. The method designs a preference-based population initialization strategy to enhance the quality and diversity of initial solutions. Reference lower bounds are constructed to guide the algorithm’s iterations in a dynamically changing solution space with stochastic processing times. A performance-aware factor is introduced to improve local exploitation, while a dynamic positioning factor accelerates global exploration. An integrated fitness evaluation mechanism ensures an objective assessment of individual solution quality. To balance conflicts among multiple objectives, a hybrid selection strategy combining diversity and quality priorities is adopted. This is integrated with a quality-constrained “elite reproduction and multiple reproduction” crossover and mutation strategy. By comparing and validating the algorithm on simulation datasets and publicly available domain datasets, experimental results show that the proposed algorithm demonstrates good effectiveness and stability.
文摘针对多目标集成工艺规划与车间调度(Integrated Process Planning and Scheduling,IPPS)问题,建立了考虑完工时间、机器负载、总流程时间和机器利用率四个优化目标的IPPS问题模型。基于模拟退火算法和NSGAII算法提出了一种两阶段的混合算法求解多目标IPPS问题。工艺规划阶段以最小化加工时间和机器负载为优化目标生成工件工艺路线,调度阶段以最小化完工时间、总流程时间和最大化机器利用率为优化目标生成调度方案,两个阶段交替迭代,完成问题求解。提出了一种工艺修正策略,对工艺阶段产生的工艺路线进行调整,来提高两个系统间的交互能力,从而提高算法的求解性能。最后设计了对比实验,用三种算法分别求解24组经典的IPPS问题案例。结果表明提出的混合算法和工艺修正策略在寻优能力和解的质量上都优于NSGAII算法,验证了提出的算法解决多目标IPPS问题的有效性。
文摘Reconfiguration,as well as optimal utilization of distributed generation sources and capacitor banks,are highly effective methods for reducing losses and improving the voltage profile,or in other words,the power quality in the power distribution system.Researchers have considered the use of distributed generation resources in recent years.There are numerous advantages to utilizing these resources,the most significant of which are the reduction of network losses and enhancement of voltage stability.Non-dominated Sorting Genetic Algorithm II(NSGA-II),Multi-Objective Particle Swarm Optimization(MOPSO),and Intersect Mutation Differential Evolution(IMDE)algorithms are used in this paper to perform optimal reconfiguration,simultaneous location,and capacity determination of distributed generation resources and capacitor banks.Three scenarios were used to replicate the studies.The reconfiguration of the switches,as well as the location and determination of the capacitor bank’s optimal capacity,were investigated in this scenario.However,in the third scenario,reconfiguration,and determining the location and capacity of the Distributed Generation(DG)resources and capacitor banks have been carried out simultaneously.Finally,the simulation results of these three algorithms are compared.The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios.Specifically,the energy losses were reduced from 211 to 51.35 kW(a 75.66%reduction),119.13 kW(a 43.54%reduction),and 23.13 kW(an 89.04%reduction),while the voltage stability index(VSI)decreased from 6.96 to 2.105,1.239,and 1.257,respectively,demonstrating significant improvement in the voltage profile.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
