A multi-objective approach for the integrated planning of drone and robot assisted truck operations in last-mile delivery

Supply chains are experiencing a major transition driven by changing customer expectations, environmental concerns, and technological development. Considering the surge in e-commerce since the pandemic, the path forward for affordable, responsive supply chains is autonomous last-mile delivery. Drone and robot technologies complement the last-mile delivery’s operational requirements and hence should be incorporated to assist truck deliveries. This study develops a bi-objective optimization framework for the integrated planning of Drone-And-Robot-assisted Truck (DART) delivery operations to minimize total delivery cost and maximize customer satisfaction considering a soft time window. Three models, including DART, drone-, and robot-assisted trucks are compared considering different operational situations. The results show that the DART delivery mode outperforms with an increase in the number of demand points. DART is particularly preferred when there is a moderate combination of high-density and distant demand points in last-mile delivery. Numerical experiments confirmed that the robot-assisted delivery model brings about cost-effectiveness in heavily populated areas. On the other hand, the drone-assisted truck model stands out in situations where there is a small number of demand points with high dispersity. • Explore integrating diverse last-mile delivery modes to leverage synergies. • Creating a novel optimization model to minimize truck route time and overall costs. • Developing an adaptive multi-objective genetic algorithm for the proposed model. • Analysing and comparing results from various autonomous-assisted delivery models. • Assess the effects of location and demand density on autonomous mode efficiency.