Publication: Express shipments with autonomous robots and public transportation
Program
School / College / Institute
College of Engineering
KU-Authors
KU Authors
Co-Authors
Ermagan, Umut
Publication Date
Language
Type
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
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.
Source
Publisher
Pergamon-Elsevier Science Ltd
Subject
Economics, Engineering, civil, Operations research and management science
Citation
Has Part
Source
Transportation Research Part E: Logistics and Transportation Review
Book Series Title
Edition
DOI
10.1016/j.tre.2024.103782