Consistent routing for local same-day delivery via micro-hubs

Abstract

An increasing number of local shops offer same-day delivery in order 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 and micro-depots for consolidated transportation of parcels. At this, many stakeholders (storekeepers, drivers, and customers) need to be coordinated. Consistent routes between micro-hubs simplify the distribution process and increase reliability for all stakeholders involved. The shared vehicles thus 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 micro-hub 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. While the first stage determines the vehicle schedules, the second stage optimises the flow of realised orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a time-expanded network formulation of the problem which is solved to optimality using commercial MIP-software. We assess our model against a non-consistent upper bound and a practically-inspired heuristic to evaluate the cost of consistency and the consolidation of goods. We analyse the performance of our method for a variety of instance settings. 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 costs of consistency are surprisingly low.