The booming of mobile internet and crowdsourcing technology has offered great opportunities for first and last mile logistics (FLML) service. Unlike the traditional FLML service that separates the parcel collection in the first mile from the parcel delivery in the last mile, a new type of crowdsourced FLML service integrates parcel collection and parcel delivery services as a whole, which can significantly improve the efficiency of FLML service. Briefly, in a crowdsourced FLML service, the platform assigns the customers' triggered pick-up parcels to the couriers who are delivering drop-off parcels in terms of the real-time status of couriers (e.g., capacity, location, and schedule). Existing works solving the crowdsourced FLML problem only consider the utility maximization for the platform but ignore the incentive to the utilities of couriers. Inspired by this, in this paper, we investigate a novel type of crowdsourced FLML problem, namely Auction-based Crowdsourced FLML (ACF), where the platform assigns the couriers with suitable pick-up parcels based on the preferences of couriers with the goal of maximizing the social welfare of the platform and couriers. To solve the ACF problem, we present a novel auction model named Multi-attribute Reverse Vickrey (MRV), where the couriers bid on parcels according to their preferences for parcels. Based on the MRV model, we present three efficient assignment algorithms to assign parcels to couriers. In addition, we give theoretical analysis for our proposed algorithms. Extensive experiments examine the efficiency and effectiveness of our solutions.
Scopus Subject Areas
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Location-based services