Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off. Issue 1 (27th July 2021)

• Record Type: Journal Article
• Authors: Castillo, Vincent E.; Bell, John E.; Mollenkopf, Diane A.; Stank, Theodore P.
• Is Part Of: Journal of business logistics. Volume 43:Issue 1(2022)
• Page Start: 36
• Page End: 61
• Publication Date: 2021-07-27
• Subjects: agent‐based simulation -- crowd logistics -- last mile -- order fulfillment -- sociotechnical system; Business logistics -- Periodicals; Business logistics -- United States -- Periodicals; 658.5005
• DOI: 10.1111/jbl.12288
• Languages: English
• ISSNs: 0735-3766

Abstract

Abstract: Retail firms now offer same‐day delivery via hybrid fleets that augment a privately owned delivery fleet with crowdsourced assets. As research on crowdsourced delivery continues to grow, it has implicitly assumed deliveries being conducted with an entirely crowdsourced fleet. In this study, we adopt a sociotechnical systems perspective to consider the phenomenon of combining privately owned delivery vehicles with crowdsourced assets into a hybrid fleet. In this exploratory effort, we examine how elements of the hybrid delivery system, namely, driver autonomy, compensation, fleet size, fleet mix, and demand intensity affect cost and service in last mile delivery. Using a multimethod simulation combining agent‐based and discrete event techniques with home delivery data from a major US retail pharmacy, we show how the emergent outcomes of this hybrid delivery system are a consequence of the constituent elements' interdependence. Our experiments suggest a convex relationship between crowdsourced driver compensation and cost performance, such that low and high compensation amounts may actually increase unit delivery costs relative to a median compensation level, and a nonlinear negative relationship between compensation and fulfillment time. We find that these effects are moderated by fleet design (in terms of size and vehicle type mix) and order arrival rate intensity.