In this paper, we investigate a new variant of last-mile delivery that integrates the scheduling of static E-commerce parcels and Online-to-Offline(O2O) parcels. The O2O parcels, such as flowers and cakes, are often delivered intra city with a time window constraint. It is driven by the concept of delivery-as-a-service, which targets at building consolidated infrastructure and using the same fleet of vehicles to provide standardized delivery services for different types of merchants. We formulate it as an integration of Multi-Depot Multi-Trip Vehicle Routing Problem (MDMTVRP) and Paired Pickup and Delivery Problem with Time Window (PPDPTW). To solve the mixed problem of MDMTVRP and PPDPTW, we present its Mixed-Integer Programming (MIP) model to obtain the optimal solution for small-scale instances. To solve large-scale problems, we propose a hybrid neighborhood search strategy to effectively combine the merits of ALNS and tabu search. We also present a two-level pruning strategy that can significantly accelerate the local search procedure. We conduct extensive numeric experiments on multiple datasets, and results showed that our hybrid approach achieved near-optimal performance and established clear superiority over ALNS and tabu search.

在本文中，我们研究了最后一英里交付的新变体，该变体集成了静态电子商务包裹和在线到离线（O2O）包裹的调度。诸如鲜花和蛋糕之类的O2O包裹通常在有时间窗口限制的情况下在城市内交付。它由“交付即服务”的概念驱动，该概念旨在建立统一的基础架构，并使用相同的车队为不同类型的商人提供标准化的交付服务。我们将其公式化为多站点多行程车辆路径问题（MDMTVRP）和带时间窗的成对提货和交付问题（PPDPTW）的集成。为了解决MDMTVRP和PPDPTW的混合问题，我们提出了其混合整数编程（MIP）模型，以获得小规模实例的最佳解决方案。为了解决大规模的问题，我们提出一种混合邻域搜索策略，以有效地结合ALNS和禁忌搜索的优点。我们还提出了一种两级修剪策略，可以显着加快本地搜索过程。我们对多个数据集进行了广泛的数值实验，结果表明，我们的混合方法取得了接近最佳的性能，并建立了明显优于ALNS和禁忌搜索的优势。