This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discre...This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.展开更多
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-...沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。展开更多
针对高比例可再生能源并网过程中因波动性与间歇性导致综合能源系统供电可靠性不足的问题,文中提出一种面向新能源小镇的两阶段鲁棒优化配置策略。首先,第一阶段利用源-荷历史数据,以系统配置成本最低为目标函数,对机组容量配置进行初...针对高比例可再生能源并网过程中因波动性与间歇性导致综合能源系统供电可靠性不足的问题,文中提出一种面向新能源小镇的两阶段鲁棒优化配置策略。首先,第一阶段利用源-荷历史数据,以系统配置成本最低为目标函数,对机组容量配置进行初步决策;第二阶段采用多面体不确定集描述源-荷的不确定性,以系统运行成本最低为目标函数,结合第一阶段的决策结果,获取最恶劣场景下源-荷预测功率数据。其次,引入不确定度参数以控制鲁棒优化配置方案的保守度。然后,利用列与约束生成(column and constraint generation,C&CG)算法对模型进行求解,通过迭代更新机组容量配置,收敛得到最优配置方案。最后,以我国北方某新能源小镇为研究案例,算例结果表明所提策略与优化方法具有可行性,且能够提高新能源小镇的供电可靠性和经济性。展开更多
基金supported by the BK21 FOUR funded by the Ministry of Education of Korea and National Research Foundation of Korea,a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00438411).
文摘This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
文摘沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。
文摘针对高比例可再生能源并网过程中因波动性与间歇性导致综合能源系统供电可靠性不足的问题,文中提出一种面向新能源小镇的两阶段鲁棒优化配置策略。首先,第一阶段利用源-荷历史数据,以系统配置成本最低为目标函数,对机组容量配置进行初步决策;第二阶段采用多面体不确定集描述源-荷的不确定性,以系统运行成本最低为目标函数,结合第一阶段的决策结果,获取最恶劣场景下源-荷预测功率数据。其次,引入不确定度参数以控制鲁棒优化配置方案的保守度。然后,利用列与约束生成(column and constraint generation,C&CG)算法对模型进行求解,通过迭代更新机组容量配置,收敛得到最优配置方案。最后,以我国北方某新能源小镇为研究案例,算例结果表明所提策略与优化方法具有可行性,且能够提高新能源小镇的供电可靠性和经济性。
