Publication:
Express shipments with autonomous robots and public transportation

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College of Engineering

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Ermagan, Umut

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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.

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Pergamon-Elsevier Science Ltd

Subject

Economics, Engineering, civil, Operations research and management science

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Has Part

Source

Transportation Research Part E: Logistics and Transportation Review

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DOI

10.1016/j.tre.2024.103782

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