基于时间窗口的多充电车按需调度  

Time window based on-demand scheduling for multiple mobile chargers

作　　者：何琴 任美璇 吴迭 杨霁琳 刘唐 HE Qin;REN Mei-xuan;WU Die;YANG Ji-lin;LIU Tang(College of Computer Science,Sichuan Normal University,Chengdu 610101,China;Visual Computing and Virtual Reality Key Laboratory of Sichuan Province,Sichuan Normal University,Chengdu 610066,China)

机构地区：[1]四川师范大学计算机科学学院,四川成都610101 [2]四川师范大学可视化计算与虚拟现实四川省重点实验室,四川成都610066

出　　处：《计算机工程与设计》2025年第2期423-430,共8页Computer Engineering and Design

基　　金：国家自然科学基金项目(62072320、62002250);四川省自然科学基金项目(2022NSFSC0569、2022NSFSC0929)。

摘　　要：为解决大规模无线可充电传感器网络(wireless rechargeable sensor networks,WRSNs)能量受限问题,提出一个基于混合种群增量学习的多充电车按需充电调度方案。考虑到不同传感器节点的能耗速率差异显著,利用时间窗口表示传感器节点实际充电需求;通过引入种群增量学习思想建立能够描述充电车路径分布的概率矩阵,生成多辆移动充电车的最优充电路径。仿真实验对比了所提方案与其它调度算法在充电车数量、每辆充电车平均服务节点数与充电车总行驶距离方面的表现,实验结果验证了所提方案具有良好的性能。To address the energy-constrained problem in large-scale wireless rechargeable sensor networks(WRSNs),a multi-charger on-demand charging scheduling based on hybrid population incremental learning was presented.Considering that the energy consumption rate varies significantly among sensors,the time window was used to represent the actual charging demand.The idea of population incremental learning was introduced to establish a probability matrix that could describe the path distribution of chargers,so as to generate the optimal charging path of multiple chargers.The simulation experiments were conducted to compare the proposed scheme with other three schemes in terms of the number of mobile chargers,the average number of service nodes per charger and the total traveling distances.The results demonstrate that the proposed scheme has good performance.

关 键 词：无线可充电传感器网络 时间窗口 种群增量学习 按需充电 移动充电车 概率矩阵 充电路径 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]