Optimal Routing Strategies for Last-Mile Delivery with a Fleet of a Single Truck and Multiple Heterogeneous Robots

Abstract

To resolve challenges arising from traditional truck-based delivery systems, the integration of autonomous robots into last-mile delivery operations has recently received attention as a promising solution. This study addresses a hybrid truck-robots last-mile delivery system that encompasses both homogeneous and heterogeneous robots. To discuss battery management strategies for robots returning after rendezvousing with a truck, we propose mixed-integer linear programming models offering two options: battery recharging and battery swapping. We explore optimal solutions for truck and robot routes, as well as various robot combinations aimed at minimizing total delivery time. Additionally, we present a three-phased iterative heuristic algorithm designed to effectively solve large-sized problems. The proposed models are validated with application to Southern District, Ulsan, in the Republic of Korea, and Rapid City, South Dakota, in the U.S., serving as examples of urban and rural areas, respectively. Our findings demonstrate that the proposed truck-robots system, particularly with the battery-swapping option, can achieve up to a 50% reduction in total delivery time compared with traditional truck-only delivery systems, applicable to both urban and rural areas. Furthermore, our results underscore the importance of long driving range for the robots in urban areas for enhancing delivery system performance. We also conduct a sensitivity analysis to examine the impact of the following factors on delivery system performance: customer node size, robot speed, number of robots, and various robot ratios. This study helps fill the gap in the literature concerning hybrid truck-robot delivery systems, with implications for improving delivery efficiency and reducing total delivery time in urban areas.