采用嵌入时空距离的混合蚁群算法求解一类受限车辆路径问题

冯振辉; 肖人彬

doi:10.1631/FITEE.2200585

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采用嵌入时空距离的混合蚁群算法求解一类受限车辆路径问题

常规文章 | Updated：2023-07-24

- 采用嵌入时空距离的混合蚁群算法求解一类受限车辆路径问题
- Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints
- 信息与电子工程前沿(英文版) 2023年24卷第7期页码：1062-1079
- Affiliations：
  
  1.School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  2.Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China
  3.Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, Wuhan 430074, China
- Author bio：
  
  E-mail: feng_zh@hust.edu.cn
  ‡Corresponding author
- Funds：
  
  the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(2018AAA0101200)
- DOI：10.1631/FITEE.2200585
  中图分类号： TP399
- 收稿：2022-11-21，
  
  修回：2023-04-06，
  
  纸质出版：2023-07-0
- Accepted：
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冯振辉, 肖人彬. 采用嵌入时空距离的混合蚁群算法求解一类受限车辆路径问题[J]. 信息与电子工程前沿(英文版), 2023,24(7):1062-1079.

Zhenhui FENG, Renbin XIAO. Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1062-1079.
冯振辉, 肖人彬. 采用嵌入时空距离的混合蚁群算法求解一类受限车辆路径问题[J]. 信息与电子工程前沿(英文版), 2023,24(7):1062-1079. DOI： 10.1631/FITEE.2200585.

Zhenhui FENG, Renbin XIAO. Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1062-1079. DOI： 10.1631/FITEE.2200585.

摘要

本文研究了共享出行背景下一类受限车辆路径问题，该问题以用户订单为核心，每个订单具有预约时间限制以及起始点、目的地两个位置点转换，是典型的具有时间、空间双重约束的扩展车辆路径问题。根据该问题特征，我们建立了以运营成本最低和用户体验度最高为目标的路径规划模型。为更精确地求解模型，根据用户的时间和空间属性定义了时空距离表示函数，进而提出一种嵌入时空距离的混合蚁群算法。该算法可分为两个阶段，首先通过时空聚类，以用户之间时空距离为主要衡量指标对用户进行分类，为问题求解提供启发式信息；其次结合劳动分工策略和时空距离函数，提出一种改进蚁群算法进行优化求解，以得到最终调度路线。基于现有数据集和实际城市环境的仿真案例进行数值实验。与其他启发式算法相比，该算法将基准实例中求得的最短路径长度降低2%–14%；与其他现存路径规划算法相比，该算法在测试实例上求得的综合成本更有竞争力。最后，利用两个实际的城市环境仿真案例进一步验证了所提算法的有效性。

Abstract

We investigate a kind of vehicle routing problem with constraints (VRPC) in the car-sharing mobility environment

where the problem is based on user orders

and each order has a reservation time limit and two location point transitions

origin and destination. It is a typical extended vehicle routing problem (VRP) with both time and space constraints. We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user (or passenger) experience. To solve the scheduling model more accurately

a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer

and a spatiotemporal distance embedded hybrid ant colony algorithm (HACA-ST) is proposed. The algorithm can be divided into two stages. First

through spatiotemporal clustering

the spatiotemporal distance between users is the main measure used to classify customers in categories

which helps provide heuristic information for problem solving. Second

an improved ant colony algorithm (ACO) is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route. Computational analysis is carried out based on existing data sets and simulated urban instances. Compared with other heuristic algorithms

HACA-ST reduces the length of the shortest route by 2%–14% in benchmark instances. In VRPC testing instances

concerning the combined cost

HACA-ST has competitive cost compared to existing VRP-related algorithms. Finally

we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

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references

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