电动汽车加电站电池配送路径优化

doi:10.13190/j.jbupt.2014.03.024

北京邮电大学学报 ›› 2014, Vol. 37 ›› Issue (3): 120-124.doi: 10.13190/j.jbupt.2014.03.024

• 研究报告 • 上一篇

电动汽车加电站电池配送路径优化

修佳鹏¹, 陈晨², 杨正球¹, 刘辰²

1. 北京邮电大学软件学院, 北京 100876;
2. 北京邮电大学计算机学院, 北京 100876

收稿日期:2013-10-18 出版日期:2014-06-28 发布日期:2014-06-08
作者简介:修佳鹏（1977-），女，讲师，E-mail：xiujiapeng@bupt.edu.cn.
基金资助:
国家科技支撑计划项目（2011BAG02B00）

Research on Path Optimization of Electric Vehicle Power Station Battery Dispatching

XIU Jia-peng¹, CHEN Chen², YANG Zheng-qiu¹, LIU Chen²

1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2013-10-18 Online:2014-06-28 Published:2014-06-08

摘要/Abstract

摘要：

在分析电动汽车加电站运营模式的基础上，根据电动汽车加电站需求动态变化的特点，建立了加电站电池配送路径问题的动态车辆调度模型. 利用自适应准则改进遗传算法，构造了自适应遗传算法；针对动态车辆调度问题实时性强的特点，设计了"初始化路径制定+实时动态调度"的两阶段求解策略，通过信息更新插入动态需求加电站，对已产生的计划路径进行局部优化调整，仿真计算结果验证了模型和算法的有效性.

关键词: 加电站, 电池配送, 路径优化, 自适应遗传算法

Abstract:

On the basis of analyzing electric vehicle power station operation mode and aiming at the dynamic changes of power station requirements, a two-phase mathematic programming model based on power station center is presented for dynamic vehicle routing problem. And a new adaptive genetic algorithm is designed for dynamic vehicle routing problem. Aiming at the real time of dynamic vehicle scheduling problem, the two-phase solution of "initial path stage" and "real-time dynamic scheduling stage" are established, which optimizes sub-routes through continuously updating information and inserting the dynamic needs power stations. Simulation validates the effectiveness of the model and the algorithm.

Key words: power station, battery distribution, optimizing path, adaptive genetic algorithm

中图分类号:

P315.69

修佳鹏, 陈晨, 杨正球, 刘辰. 电动汽车加电站电池配送路径优化[J]. 北京邮电大学学报, 2014, 37(3): 120-124.

XIU Jia-peng, CHEN Chen, YANG Zheng-qiu, LIU Chen. Research on Path Optimization of Electric Vehicle Power Station Battery Dispatching[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(3): 120-124.

参考文献

[1] 蔡焕芹, 王丽亚, 高薛文. 遗传算法在预拌混凝土配送中的应用[J]. 上海交通大学学报, 2007, 41(8): 1388-1392. Cai Huanqin, Wang Liya, Gao Xuewen. Optimizing the scheduling of dispatching ready mixed concret trucks through genetic algorithm[J]. Journal of Shanghai Jiaotong University, 2007, 41(8): 1388-1392.

[2] 侯玲娟, 周泓, 梁春华. 不确定需求和旅行时间下的车辆路径问题[J]. 计算机集成制造系统, 2011, 17(1): 101-108. Hou Lingjuan, Zhou Hong, Liang Chunhuan. Vehicle routing problem with uncertain demand and travel time[J]. Computer Integrated Manufacturing Systems, 2011, 17(1): 101-108.

[3] 张飞舟, 耿嘉洲, 程鹏. 基于云遗传算法的公交车辆智能调度[J]. 武汉大学学报: 信息科学版, 2010, 35(8): 905-908. Zhang Feizhou, Geng Jiazhou, Cheng Peng. Intelligent dispatching of public vehicles based on cloud genetic algorithms[J]. Geomatics and Information Science of Wuhan University, 2010, 35(8): 905-908.

[4] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Trans on Systems, Man and Cybernetics, 1994, 24(4): 656-667.

[5] 李剑, 景博. 自适应遗传算法在多边多议题协商中的应用[J]. 北京邮电大学学报, 2008, 31(6): 67-69. Li Jiang, Jing Bo. Adaptive genetic algorithm and its application in multi-lateral multi-issue negotiation[J]. Journal of Beijing University of Posts and Telecommunication, 2008, 31(6): 67-69.

[6] 庄家俊, 刘琼. 自适应遗传优化BP网络的研究与应用[J]. 北京邮电大学学报, 2012, 35(5): 41-45. Zhuang Jiajun, Liu Qiong. Research and application of an optimized BP neural network based on adaptive genetic algorithm[J]. Journal of Beijing University of Posts and Telecommunication, 2012, 35(5): 41-45.

[7] 郑岩, 黄荣怀, 战晓苏, 等. 基于遗传算法的动态模糊聚类[J]. 北京邮电大学学报, 2005, 28(1): 75-78. Zheng Yan, Huang Ronghuai, Zhan Xiaosu, et al. Dynamic fuzzy clustering method based on genetic algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2005, 28(1): 75-78.

电动汽车加电站电池配送路径优化

Research on Path Optimization of Electric Vehicle Power Station Battery Dispatching

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	马庆刘, 喻鹏, 吴佳慧, 熊翱, 颜拥. 基于深度强化学习的综合能源业务通道优化机制[J]. 北京邮电大学学报, 2020, 43(2): 87-93.
[2]	蔚承英, 吴兰, 刘焕淋, 陈勇. 编码链路最少的多源光组播路由[J]. 北京邮电大学学报, 2017, 40(5): 87-91.
[3]	庄家俊刘琼. 自适应遗传优化BP网络的研究与应用[J]. 北京邮电大学学报, 2012, 35(5): 41-45.
[4]	李剑1,2, 景博2, 杨义先1. 自适应遗传算法在多边多议题协商中的应用[J]. 北京邮电大学学报, 2008, 31(6): 67-70.
[5]	徐跃刚，韩大海，张杰，顾畹仪. SDH虚级联中的最优化路径选择[J]. 北京邮电大学学报, 2007, 30(1): 58-61.
[6]	王海星申金升. 智能蚁群算法解决公交区域调度问题研究[J]. 北京邮电大学学报, 2006, 29(s2): 30-34.
[7]	韩玲, 孔令山, 曾志民, 丁炜. 一种分布式的PCPO单播路由算法[J]. 北京邮电大学学报, 2004, 27(5): 70-74.