The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retri...The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.展开更多
The popularization of EVs(electric vehicles) has brought an increasingly heavy burden to the development of charging facilities. To meet the demand of rapid energy supply during the driving period, it is necessary to ...The popularization of EVs(electric vehicles) has brought an increasingly heavy burden to the development of charging facilities. To meet the demand of rapid energy supply during the driving period, it is necessary to establish a fast charging station in public area. However, EVs arrive at the charging station randomly and connect to the distribution network for fast charging, it causes the grid power to fluctuate greatly and the peak-valley loads to alternate frequently, which is harmful to the stability of distribution network. In order to reduce the power fluctuation of random charging, the energy storage is used for fast charging stations. The queuing model is determined to demonstrate the load characteristics of fast charging station, and the state space of fast charging station system is described by Markov chain. After that the power of grid and energy storage is quantified as the number of charging pile, and each type of power is configured rationally to establish the random charging model of energy storage fast charging station. Finally, the economic benefit is analyzed according to the queuing theory to verify the feasibility of the model.展开更多
The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preempt...The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.展开更多
High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await i...High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.661403234)Shandong Provincial Science and Techhnology Development Plan of China(Grant No.2014GGX106009)
文摘The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.
基金Supported by National Key Research Program of China(2016YFB0101800)SGCC Scientific and Technological Project(520940170017)State Grid Shanghai Municipal Electric Power Company Scientific and Technological Projects(5209001500KP)
文摘The popularization of EVs(electric vehicles) has brought an increasingly heavy burden to the development of charging facilities. To meet the demand of rapid energy supply during the driving period, it is necessary to establish a fast charging station in public area. However, EVs arrive at the charging station randomly and connect to the distribution network for fast charging, it causes the grid power to fluctuate greatly and the peak-valley loads to alternate frequently, which is harmful to the stability of distribution network. In order to reduce the power fluctuation of random charging, the energy storage is used for fast charging stations. The queuing model is determined to demonstrate the load characteristics of fast charging station, and the state space of fast charging station system is described by Markov chain. After that the power of grid and energy storage is quantified as the number of charging pile, and each type of power is configured rationally to establish the random charging model of energy storage fast charging station. Finally, the economic benefit is analyzed according to the queuing theory to verify the feasibility of the model.
基金supported by national fundamental research key project (No. JCKY2017203B082)
文摘The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.
基金supported by Research and Innovation Projects for Graduates of Jiangsu Graduates of Jiangsu Province (No. CXZZ12 0483)the Science and Technology Support Program of Jiangsu Province (No. BE2012849)
文摘High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.
