Express shipments with autonomous robots and public transportation

Umut Ermağan, Barış Yıldız and F. Sibel Salman

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 192, issue C

Abstract: 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.

Keywords: Express shipment; Public transportation; Autonomous robots; Rolling horizon; Machine learning; Sustainable logistics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103782

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