On-demand delivery has become increasingly popular around the world. Brick-and-mortar grocery stores, restaurants, and pharmacies are providing fast delivery services to satisfy the growing home delivery demand. Motivated by a large meal and grocery delivery company, we model and solve a multiperiod driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The operator of this system needs to dispatch a set of drivers and specify their delivery routes in a stochastic environment, in which random demand arrives over a fixed number of periods. The resulting dynamic program is challenging to solve due to the curse of dimensionality. We propose a novel approximation framework to approximate the value function via a simplified dispatching program. We then develop efficient exact algorithms for this problem based on Benders decomposition and column generation. We validate the superior performance of our framework and algorithms via extensive numerical experiments. Tested on a real-world data set, we quantify the value of adaptive dispatching and routing in on-time delivery and highlight the need of coordinating these two decisions in a dynamic setting. We show that dispatching multiple vehicles with short trips is preferable for on-time delivery, as opposed to sending a few vehicles with long travel times.

中文翻译：

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在动态和随机环境中按时交付的多周期调度和路​​由

按需交付在世界范围内变得越来越流行。实体杂货店、餐馆和药房正在提供快速送货服务，以满足日益增长的送货上门需求。受一家大型餐饮和杂货配送公司的启发，我们为以准时性能为主要目标的最后一英里配送系统建模并解决了多周期司机调度和路线问题。该系统的运营商需要在随机环境中调度一组司机并指定他们的送货路线，其中随机需求在固定数量的时间段内到达。由于维数灾难，由此产生的动态程序很难解决。我们提出了一种新颖的近似框架，通过简化的调度程序来近似值函数。然后，我们基于 Benders 分解和列生成为这个问题开发了有效的精确算法。我们通过大量的数值实验验证了我们的框架和算法的卓越性能。在真实世界的数据集上进行测试，我们量化了自适应调度和路由在准时交付中的价值，并强调了在动态环境中协调这两个决策的必要性。我们表明，与发送几辆旅行时间较长的车辆相比，调度多辆短途车辆更适合按时交付。我们量化了自适应调度和路由在准时交付中的价值，并强调了在动态环境中协调这两个决策的必要性。我们表明，与发送几辆旅行时间较长的车辆相比，调度多辆短途车辆更适合按时交付。我们量化了自适应调度和路由在准时交付中的价值，并强调了在动态环境中协调这两个决策的必要性。我们表明，与发送几辆旅行时间较长的车辆相比，调度多辆短途车辆更适合按时交付。