Express shipments with autonomous robots and public transportation

Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort. • A novel public transport- and Autonomous Robots (ARs)-based delivery system. • A machine learning-enhanced methodology to solve the real-time dispatching problem. • Exploration of three re-positioning policies for ARs to consider future orders. • Experiments with real-world data that show the potential of the proposed system.