Consistent Routing for Local Same-Day Delivery via Micro-Hubs

Charlotte Ackva and Marlin W. Ulmer

No 22007, FEMM Working Papers from Otto-von-Guericke University Magdeburg, Faculty of Economics and Management

Abstract: An increasing number of local shops offer local same-day delivery to compete with the online giants. However, the distribution of parcels from individual shops to customers reduces the rare consolidation opportunities in the last mile even further. Thus, shops start collaborating on urban same-day delivery by using shared vehicles for consolidated transportation of parcels. The shared vehicles conduct consistent daily routes between micro-hubs in the city, serving as transshipment and consolidation centres. This allows stores to bring orders to the next micro-hub, where the parcel is picked up by a vehicle and delivered to the microhub closest to its destination – if it is feasible with respect to the vehicle’s consistent daily schedule. Creating effective schedules is therefore very important. The difficulty of finding an effective consistent route is amplified by the daily uncertainty in order placements. We model the problem as a two-stage stochastic program. The first stage determines the vehicle schedules. The second stage optimises the flow of realized orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a multiple scenario approach and suggest problem-specific consensus functions for this framework. We assess the method’s performance against an upper bound, a practically-inspired heuristic, and the original consensus function. Our approach clearly outperforms the practically-inspired heuristic and the original consensus function. We observe that collaborative delivery via micro-hubs is worthwhile for delivery time promises of two hours or more. Noticeably, for these service promises, the cost of consistency are surprisingly low.

Keywords: micro-hubs; same-day delivery; routing consistency; two-stage stochastic programming; multiple scenario approach (search for similar items in EconPapers)
Date: 2022
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