This paper presents a set-partitioning formulation and a novel decomposition heuristic (D-H) solution algorithm to solve large-scale instances of the urban crowdsourced shared-trip delivery (CSD) problem. The CSD problem involves dedicated vehicles (DVs) and shared personal vehicles (SPVs) fulfilling delivery orders, wherein the SPVs have their own trip origins and destinations. The D-H begins by assigning as many package delivery orders (PDOs) to SPVs as possible, where the D-H enumerates the set of routes each SPV can feasibly traverse and then solves a PDO-SPV-route assignment problem. For PDO-DV assignment and DV routing, the D-H solves a multi-vehicle routing problem with time-window, tour duration, and capacity constraints using an insertion heuristic. Finally, the D-H seeks potential solution improvements by switching PDOs between SPV and DV routes through a simulated annealing (SA)-inspired procedure. The D-H outperforms a commercial solver in terms of computational efficiency while obtaining near-optimal solutions for small problem instances. The SA-inspired switching procedure outperforms a large neighborhood search algorithm regarding run time, and the two are comparable regarding solution quality. Finally, the paper uses the D-H to analyze the impact of several relevant factors on city-scale CSD system performance, namely the number of participating SPVs and the maximum willingness to detour of SPVs. Consistent with the existing literature, we find that CSD can substantially reduce delivery costs. However, we find that CSD can increase vehicle miles traveled. Our findings provide meaningful insights for logistics practitioners, while the algorithms illustrate promise for large real-world systems.

翻译：本文提出了一种集合划分模型及一种新型分解启发式算法，用于求解大规模城市众包共享行程配送问题。该问题涉及专用车辆与共享私人车辆共同完成配送订单，其中共享私人车辆拥有自身的行程起点与终点。分解启发式算法首先尽可能多地将包裹配送订单分配给共享私人车辆，具体通过枚举每辆共享私人车辆所有可行的路径集合，随后求解一个包裹配送订单-共享私人车辆-路径分配问题。对于包裹配送订单与专用车辆的分配及专用车辆路径规划，该算法采用插入启发式方法求解一个带时间窗、行程时长及容量约束的多车辆路径问题。最后，算法通过模拟退火启发的交换流程，探索在共享私人车辆与专用车辆路径间转移包裹配送订单以寻求潜在改进。对于小规模问题实例，分解启发式算法在计算效率上优于商业求解器，并能获得近似最优解。模拟退火启发的交换流程在运行时间上优于大邻域搜索算法，两者在解质量方面表现相当。最后，本文利用该分解启发式算法分析了若干相关因素对城市级众包共享行程配送系统性能的影响，即参与共享私人车辆的数量及其最大绕行意愿。与现有文献一致，我们发现众包共享行程配送能显著降低配送成本。然而，研究也表明该系统可能增加车辆行驶里程。本研究为物流从业者提供了有价值的见解，同时所提算法展现了处理大规模实际系统的应用潜力。