Towards a new last-mile delivery system: Cost and energy-optimized robot and van allocation

In recent years, autonomous delivery robots have gained tremendous momentum in last-mile logistics operations due to their potential to reduce costs, emissions, and congestion while providing access to narrow roads. However, their operation is restricted by distance, capacity, and maintenance needs. These limitations necessitate a combination of robot and van operations, wherein vans deliver non-bulky parcels to robots while performing bulky and distant deliveries themselves. Building on insights from the Helsingbotica project in Sweden, which includes key stakeholders such as VTI, Hugo, Apotea, BEST Transport, and the City of Helsingborg, this paper aims to evaluate the potential of a joint robot-van setup by analyzing the cost and energy savings of this approach at different geographic scales and configurations. We propose a universally applicable parcel demand estimation framework that uses OpenStreetMap building density and road network data to simulate parcel demand and create meaningful service zones using K-Medoids clustering. The estimated demand and constructed zones serve as input for an integer programming model that assigns parcels to robots and vans in a cost-and energy-saving manner, considering restrictions such as distance and parcel weight. The model's parameters are calibrated based on structured workshops with industry partners. Our results demonstrate that integrating autonomous delivery robots can reduce operational costs by up to 57% and energy consumption by up to 42%, depending on the configuration. Thus, this study concludes that integrating robots into last-mile delivery can enhance the flexibility and efficiency of logistics service providers, offering a sustainable solution for urban deliveries.