• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇    下一篇

A*估价函数在复杂配送路径优化中的改进

李世明1,2,王玉芬1,张秉桢1,李秋月1   

  1. (1.哈尔滨师范大学计算机科学与信息工程学院,黑龙江 哈尔滨 150025;
    2.上海市信息安全综合管理技术研究重点实验室,上海 200240)
  • 收稿日期:2018-09-12 修回日期:2019-02-02 出版日期:2019-10-25 发布日期:2019-10-25
  • 基金资助:

    黑龙江省自然科学基金(F2016030,F2018023);黑龙江省教育厅科学技术研究项目(12511147);上海市信息安全管理技术研究重点实验室开放课题(AGK2015003)

Improvement of A* valuation function
in complex distribution path optimization
 

LI Shi-ming1,2,WANG Yu-fen1,ZHANG Bing-zhen1,LI Qiu-yue1   

  1. (1.College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025;
    2.Shanghai Key Laboratory of Integrated Administration Technologies for Information Security,Shanghai 200240,China)
  • Received:2018-09-12 Revised:2019-02-02 Online:2019-10-25 Published:2019-10-25

摘要:

随着城市交通日趋复杂,时间和路径成本直接决定路径规划的效果,但传统的A*算法已经不能满足复杂路径优化的需求。对此,提出了一种TWA*算法,在传统的A*算法基础上对其估价函数进行了改进。首先,通过时间参数建立时间因子归一化模型来提高节点被选择概率,节约时间成本;其次,结合时间因子与估价函数降低路程成本。实验采用北京市某一区域GPS数据,分别用A*算法和TWA*算法进行验证,结果表明,与传统A*算法相比,TWA*算法在时间及路径成本上分别提高了约6%和5%,达到了路径优化的目的,同时为企业物流的高效配送提供了较可靠的参考依据。

关键词: A*算法, 路径优化, 估价函数, 物流配送

Abstract:

As urban traffic becomes more and more complex, time and path cost directly determine the effect of path planning. The traditional A* algorithm cannot satisfy these needs any more. We thus propose a TWA* algorithm, which can improve its evaluation function on the basis of the original A* algorithm. Firstly, a time factor normalization model is established by time parameter to expand the probability of node selection and save time cost. Secondly, it is combined with the valuation function to reduce the distance cost. The A* algorithm and TWA* algorithm are respectively verified in the experiment using the GPS data of a region of Beijing. The results show that the TWA* algorithm improves the time and path cost  by about 6% and 5% respectively, achieving the goal of path optimization and providing reliable reference basis for efficient delivery of enterprise logistics.
 

Key words: A* algorithm, path optimization, valuation function, logistics distribution