We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time-dependent travel times. While dedicated vehicles are available throughout the day, the availability of crowdshippers is unknown a priori and they appear randomly for only portions of the day. With an objective of minimizing the sum of routing costs, piece-rate crowdshipper payments, and lateness charges, we model the uncertainty in request arrivals and crowdshipper appearances as a Markov decision process. To determine an action at each decision epoch, we employ a heuristic that partially destroys the existing routes and repairs them under the guidance of a parameterized cost function approximation that accounts for the remaining temporal capacity of delivery vehicles. We benchmark our real-time heuristic with an adaptive large neighborhood search and demonstrate the effectiveness of our method with several performance metrics. In addition, we conduct computational experiments to demonstrate the impact of inserting wait time in the route scheduling and the benefit of explicitly modeling time-dependent travel times. Through our computational testing, we also investigate the potential of demand management mechanisms that facilitate many-to-one request bundles or one-to-many request bundles to reduce the cost to service requests.

中文翻译：

---

适用于协作平台的动态取货和配送功能，支持时间依赖性的差旅和众包运输


我们研究了一个提货和配送问题，当客户在一天内从协作电子商务门户上的供应商选择中随机提交请求时，就会出现该问题。根据客户请求的属性，调度员将配送服务动态安排在专用车辆或 Crowdshipper 上，这两者都会经历与时间相关的旅行时间。虽然全天都有专用车辆可用，但 Crowdshipper 的可用性是先验的未知数，它们仅在一天中的部分时间随机出现。为了最大限度地减少路由成本、计件 Crowdshipper 付款和延迟费用的总和，我们将请求到达和 Crowdshipr 出现的不确定性建模为马尔可夫决策过程。为了确定每个决策时期的行动，我们采用了一种启发式方法，该启发式方法部分销毁现有路线，并在参数化成本函数近似的指导下修复它们，该近似值考虑了送货车辆的剩余时间容量。我们使用自适应大型邻域搜索对我们的实时启发式方法进行基准测试，并通过几个性能指标来证明我们的方法的有效性。此外，我们还进行了计算实验，以证明在路线调度中插入等待时间的影响，以及显式建模与时间相关的旅行时间的好处。通过我们的计算测试，我们还研究了需求管理机制的潜力，这些机制可以促进多对一请求捆绑包或一对多请求捆绑包，以降低服务请求的成本。