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.
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.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Renbin XIAO and Zhenhui FENG designed the research. Zhenhui FENG proposed the approach, performed the experiments, and drafted the paper. Renbin XIAO revised and finalized the paper.
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Zhenhui FENG and Renbin XIAO declare that they have no conflict of interest.
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Project supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China (No. 2018AAA0101200)
List of supplementary materials
1 Supplement to the VRPC model
2 Supplement to the HACA-ST algorithm
3 Supplement to numerical examples
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Feng, Z., Xiao, R. Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints. Front Inform Technol Electron Eng 24, 1062–1079 (2023). https://doi.org/10.1631/FITEE.2200585
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DOI: https://doi.org/10.1631/FITEE.2200585
Key words
- Vehicle routing problem with constraints (VRPC)
- Spatiotemporal distance function
- Labor division strategy
- Ant colony algorithm (ACO)