灾后救援中带无人机的两梯队覆盖巡游车辆路径问题
A Two-Echelon Covering Tour Vehicle Routing Problem with Drones for Post-Disaster Relief
- Technical University of Denmark（丹麦技术大学）
- Nanyang Technological University（南洋理工大学）
- Singapore Management University（新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对灾后救援物资分配，提出两梯队覆盖巡游车辆路径问题（2E-CTVRP），构建混合整数线性规划并设计GRASP-ILS-PR混合元启发式算法，在110个实例上快速求得最优解并显著改进商业求解器结果，同时分析车队配置对到达时间与公平性的影响。
AI中文摘要：
我们提出了两梯队覆盖巡游车辆路径问题（2E-CTVRP），用于灾后救援物资的分配。在第一梯队中，一支卡车车队将物资和无人机从中央仓库运送到受灾区域边缘的卫星点。在第二梯队中，从卫星点并行发射的无人机将物资运送到受害者群集的质心，这些群集是通过对受害者位置进行聚类得到的，每辆卡车在卫星点等待其无人机返回。该问题结合了卫星点分配给卡车、卡车路线排序以及群集分配给卫星点，并最小化卡车到达卫星点和仓库的时间总和。我们将2E-CTVRP建模为混合整数线性规划，并提出了一种混合元启发式算法GRASP-ILS-PR，该算法结合了贪婪随机化构造、单轨迹搜索以及针对群集分配给卫星点的周期性路径重连。在一个包含110个实例的新基准集上，GRASP-ILS-PR在30秒内找到了所有已知最优解，在大多数较大实例上匹配或改进了商业求解器的一小时解，并在包含50个卫星点的实例上将其改进最多达11%。它明显优于采用进化路径重连的传统GRASP，并且比使用相同搜索组件的模拟退火算法产生更一致的结果。对两种车队配置的分析表明，在大多数实例中，少量大型卡车能最小化累计到达时间，而在每个实例中，大量小型卡车能更早且更公平地向受害者运送物资。
英文摘要：
We introduce the two-echelon covering tour vehicle routing problem (2E-CTVRP) for the distribution of relief supplies after a disaster. In the first echelon, a fleet of trucks transports supplies and drones from a central depot to satellites at the periphery of the affected area. In the second echelon, drones launched in parallel from the satellites deliver the supplies to the centroids of victim clusters, which are obtained by clustering the victim locations, and each truck waits at a satellite until its drones have returned. The problem combines the assignment of satellites to trucks, the sequencing of the truck routes, and the assignment of clusters to satellites, and minimizes the sum of the arrival times of the trucks at the satellites and at the depot. We formulate the 2E-CTVRP as a mixed integer linear program and propose a hybrid metaheuristic, GRASP-ILS-PR, which combines greedy randomized construction, a single-trajectory search, and periodic path relinking on the assignment of clusters to satellites. On a new benchmark set of 110 instances, GRASP-ILS-PR finds all known optimal solutions within 30 seconds, matches or improves on the one-hour solutions of a commercial solver on most larger instances, and improves on them by up to 11% for instances with 50 satellites. It clearly outperforms a conventional GRASP with evolutionary path relinking and yields more consistent results than a simulated annealing algorithm that uses the same search components. An analysis of two fleet configurations shows that few large trucks minimize the cumulative arrival time in most instances, whereas many small trucks deliver supplies to the victims earlier and more equitably in every instance.
