动态道路网络中食品配送的分批与匹配
Batching and Matching for Food Delivery in Dynamic Road Networks
摘要：
给定食品订单流和可用的送货车辆，如何将订单分配给车辆，以最大限度地缩短送货时间？必须做出几个决定：（1）将订单分配给车辆，（2）将订单分组为批次以应对有限的车辆可用性，以及（3）适应交付车辆的动态位置。我们证明了极小化问题不仅是NP-困难的，而且在多项式时间内是不可逼近的。为了缓解这一计算瓶颈，我们开发了一种算法称为FoodMatch，它将车辆分配问题映射到二分图上的最小权重完美匹配问题。为了进一步降低二分图的二次构造成本，我们部署了最佳优先搜索，只计算极有可能包含最小匹配的子图。解决方案的质量进一步提高，通过减少图聚类问题，并通过角距离预测车辆的动态位置。对来自大都市的食品配送数据进行的广泛实验表明，FoodMatch在许多指标上都比基线策略要好得多，同时足以处理现实世界的工作负载。
英文摘要：
Given a stream of food orders and available delivery vehicles, how should orders be assigned to vehicles so that the delivery time is minimized? Several decisions have to be made: (1) assignment of orders to vehicles, (2) grouping orders into batches to cope with limited vehicle availability, and (3) adapting to dynamic positions of delivery vehicles. We show that the minimization problem is not only NP-hard but inapproximable in polynomial time. To mitigate this computational bottleneck, we develop an algorithm called FoodMatch, which maps the vehicle assignment problem to that of minimum weight perfect matching on a bipartite graph. To further reduce the quadratic construction cost of the bipartite graph, we deploy best-first search to only compute a subgraph that is highly likely to contain the minimum matching. The solution quality is further enhanced by reducing batching to a graph clustering problem and anticipating dynamic positions of vehicles through angular distance. Extensive experiments on food-delivery data from large metropolitan cities establish that FoodMatch is substantially better than baseline strategies on a number of metrics, while being efficient enough to handle real-world workloads.
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