This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.U...This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue.After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle.The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs.To maximize the total expected discounted return, we formulate a Markov decision process(MDP) model for this system.The value iteration method is employed to characterize the optimal policy as a hedging point policy.Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.展开更多
随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预...随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预定时间内完成充电需求;其次,采用基于熵权法确定适应度函数权重的遗传模拟退火算法(GASA),提出一种面向多目标优化的PREV充电调度策略,综合优化停车场运营商利润和车主充电满意度。实验结果表明,基于GASA的PREV充电调度策略性能良好,与极端情况(车辆数为600的无序充电)相比,该策略的运营商利润和车主充电满意度分别提高了12.3%和109.7%,综合适应度函数值增加了35.2%;另外,其能够有效平缓配电网负荷分布,在保障配电网安全前提下实现停车场运营商和PREV车主的双赢。展开更多
基金supported by the National Social Science Fund of China (19BGL100)。
文摘This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue.After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle.The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs.To maximize the total expected discounted return, we formulate a Markov decision process(MDP) model for this system.The value iteration method is employed to characterize the optimal policy as a hedging point policy.Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.
文摘随着私家电动汽车(private electric vehicles,PREV)的普及,大规模PREV的无序充电将引起用电负荷高峰,影响配电网安全。针对商业停车场环境下的PREV充电问题,首先提出一种车辆准入机制,尽可能提高车辆准入数量,并确保准入车辆能够在预定时间内完成充电需求;其次,采用基于熵权法确定适应度函数权重的遗传模拟退火算法(GASA),提出一种面向多目标优化的PREV充电调度策略,综合优化停车场运营商利润和车主充电满意度。实验结果表明,基于GASA的PREV充电调度策略性能良好,与极端情况(车辆数为600的无序充电)相比,该策略的运营商利润和车主充电满意度分别提高了12.3%和109.7%,综合适应度函数值增加了35.2%;另外,其能够有效平缓配电网负荷分布,在保障配电网安全前提下实现停车场运营商和PREV车主的双赢。
