Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders
Iman Dayarian, Martin Savelsbergh
Abstract
Same‐day delivery of online orders is becoming an indispensable service for large retailers. We explore an environment in which in‐store customers supplement company drivers and deliver online orders on their way home. We consider a highly dynamic and stochastic same‐day delivery environment in which online orders as well as in‐store customers willing to make deliveries arrive throughout the day. Studying settings in which delivery capacity is uncertain is novel and practically relevant. Our proposed approaches are simple, yet produce high‐quality solutions in a short amount of time that can be employed in practice. We develop two rolling horizon dispatching approaches: a myopic one that considers only the state of the system when making decisions, and one that also incorporates probabilistic information about future online order and in‐store customer arrivals. We quantify the potential benefits of a novel form of crowdshipping for same‐day delivery and demonstrate the value of exploiting probabilistic information about the future. We explore the advantages and disadvantages of this form of crowdshipping and show the impact of changes in environment characteristics, for example, online order arrival pattern, company fleet size, and in‐store customer compensation on its performance, that is, service quality and operational cost.