Advancing the Urban Parcel Delivery System Using Crowdshipping
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This research presents a methodological investigation on the design, modeling, and evaluation of two types of crowdsourcee-enabled urban parcel delivery systems. The first system couples the existing truck-delivery practice with the locally available crowdsourcees to minimize the cost of last mile delivery. The second system considers the delivery of parcels without the use of any intermediate relay point i.e. crowdsourcees are solely responsible for the pickup of parcels and delivering it to customers’ doorsteps. For the first system, we consider cyclists and pedestrians as crowdsourcees who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsourcees express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsourcees’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier’s problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsourcees to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsourcee-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using off-the-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsourcees, and the crowdsourcee mode. For the second system, a new mechanism design based model is proposed in which a Delivery Service Provider (DSP) solicits ordinary individuals, i.e., crowdsourcees, who walk, bike, or drive to do delivery in urban areas. As an essential part of the model, the DSP collects private information such as one’s willingness-to-do-crowdshipping (WTDC) and available time window from crowdsourcees in order to assign shipments with the minimum cost. The mechanisms embedded in the crowdshipping model recognize that crowdsourcees may strategically misreport their private information to gain self-interest, and devises a joint shipment assignment-payment scheme that aligns the self-interest of the crowdsourcees with the objective of the DSP. Both static and dynamic cases are investigated. Numerical results demonstrate that the proposed mechanisms will lead to reduced shipping cost compared to the state-of-the-practice crowdshipping. A case study of North Chicago is also presented to further demonstrate the promise of the proposed mechanism to the on-demand delivery in urban areas.
SubjectCrowdshipping, Vehicle-routing problem, Tabu search heuristics, Mechanism design