Crowdsourced Delivery: A Dynamic Pickup and Delivery Problem with Ad-hoc Drivers

The trend towards shorter delivery lead-times reduces operational efficiency and increases transportation costs for internet retailers. Mobile technology, however, creates new opportunities to organize the last-mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place to make deliveries. We consider a peer-to-peer platform that automatically creates matches between parcel delivery tasks and ad-hoc drivers. The platform also operates a fleet of backup vehicles to serve the tasks that cannot be served by the ad-hoc drivers. The matching of tasks, drivers and backup vehicles gives rise to a new variant of the dynamic pick-up and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the various subproblems. In order to investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the environment and the behavior of the ad-hoc drivers. The results suggest that the use of ad-hoc drivers has the potential to make the last-mile more cost-efficient and can reduce the system-wide vehicle-miles.

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