Providing the crowd-sourced delivery capacity and hence enabling the practice, occasional couriers (OC) are the most critical resource in crowd-shipping (CS). Therefore, as well as establishing and retaining a solid OC base, using the OC trips efficiently is of utmost importance for the viability of the CS applications. One auspicious idea to enhance the efficiency, i.e., cover a larger demand set with the available OC trips, is to use transshipments (deliver packages with a coordinated effort of OCs) and collect OC trip information in advance to efficiently coordinate them, which gives rise to the package routing problem with registered couriers (PRP-R) we introduce in this paper. In particular, we study a CS model in which the couriers register their trips in advance while the express shipping demands arrive through a stochastic process, and the network management needs to dynamically decide package-courier assignments to carry out deliveries in the most efficient way. We develop a novel rolling horizon algorithm to solve this challenging problem in real-time, which explicitly considers the limited OC capacities and use of a back-up delivery capacity (company-owned or third party provided) to ensure the service quality. Beyond the classical rolling horizon approaches, the suggested methodology uses a novel Monte Carlo procedure to take anticipated future system conditions into account, and thus can provide package-courier assignments that have almost the same cost with the optimal solution of the static version of the problem where all demand arrivals are known a-priory. The comprehensive numerical experiments attest to the efficacy of our methodology for the real-time management of the CS operations and provide significant managerial insights about the design of CS networks.

由于提供了众包交付能力，因此使实践成为可能，偶尔快递员（OC）是众筹（CS）中最关键的资源。因此，除了建立和保留坚实的OC基础外，有效地使用OC跳闸对于CS应用程序的可行性至关重要。一个提高效率的吉祥想法是，使用可用的OC行程满足更大的需求集，即使用转运（在OC的共同努力下交付包裹）并提前收集OC行程信息以有效地进行协调，这引起了人们的关注。在本文中，我们将介绍注册快递公司（PRP-R）的包裹路由问题。特别是，我们研究了一种CS模型，在该模型中，快递员预先记录他们的行程，而快递需求是通过随机过程到达的，网络管理部门需要动态地决定包裹快递人员的分配，以最有效的方式进行交付。我们开发了一种新颖的滚动视域算法来实时解决这一难题，该算法明确考虑了有限的OC能力和使用备用交付能力（公司拥有或第三方提供）以确保服务质量。除了经典的滚动视野方法外，建议的方法还使用新颖的蒙特卡洛程序来考虑预期的未来系统条件，从而可以提供成本几乎相同的包裹快递员，并且可以静态解决问题的最佳方案所有需求到达的价格都是已知的。