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Innocente - Dynamic Assignment for a Crowdshipping Platform.pdf
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Driven by the growth of online shopping, parcel delivery has grown rapidly in recent years, resulting in an increased demand for cost-efficient last-mile delivery [3]. At the same time, the question of sustainability is increasingly present in logistics and in society in general. Crowdshipping applies the concept of crowdsourcing to the personalized delivery of freight, turning ordinary citizens into couriers for the distribution of small items. In this collaborative delivery system, individuals already traveling from an origin to a destination take charge of all or part of the delivery, taking a package along with them and making a stop along the way to drop it off. In a successful implementation, crowdshipping reduces the number of freight delivery trucks, benefiting companies by reducing their delivery costs, as well as improving sustainability [4]. The matching of crowdshippers to parcels as they appear in real time is key for crowdshipping to yield its intended effects. Indeed, the high degree of uncertainty inherent to crowdshipping, primarily in crowdshippers' and parcels' availability, can result in high operational costs and low service reliability [2]. In determining the assignment of crowdshippers to parcels over time, the main questions are as follows: (1) To which crowdshipper should we assign the parcels? (2) Should we postpone some delivery in the hope for a future crowdshipper with a shorter detour? (3) How can we consider the future uncertainty and downstream cost in real-time decision making? (4) Can we increase the total number of served parcels and/or decrease delivery distance by making smarter assignment decisions? Interestingly, the majority of research on crowdsourced transportation does not directly address the challenges of uncertain availability, but rather assume perfect information. Studies that incorporate the uncertainty in crowdshipping operations are scarce, and only few of those consider a dynamic problem setting (e.g., [1]; [5]; [7]; and [2]). Recent works by Mousavi et al. [2] and Dayarian and Savelsbergh [1] are the closest to ours. However, both consider in-store customers as crowdshippers to deliver groceries within few hours.
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Innocente, E., & Tancrez, J.-S. (2024, February 9). Dynamic Assignment for a Crowdshipping Platform. ORBEL38, University of Antwerp. https://hdl.handle.net/2078.5/276761