Ahadi, Ramin ORCID: 0000-0002-8447-5008, Ketter, Wolfgang ORCID: 0000-0001-9008-142X, Collins, John ORCID: 0000-0002-7881-4595 and Daina, Nicolò ORCID: 0000-0002-5902-4651 (2022). Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets. Transportation science, 57 (3). pp. 613-630. CATONSVILLE: INFORMS. ISSN 1526-5447
Full text not available from this repository.Abstract
We study the operational problem of shared autonomous electric vehicles that cooperate in providing on-demand mobility services while maximizing fleet profit and service quality. Therefore, we model the fleet operator and vehicles as interactive agents enriched with advanced decision-making aids. Our focus is on learning smart charging policies (when and where to charge vehicles) in anticipation of uncertain future demands to accommodate long charging times, restricted charging infrastructure, and time-varying electricity prices. We propose a distributed approach and formulate the problem as a semiMarkov decision process to capture its stochastic and dynamic nature. We use cooperative multiagent reinforcement learning with reshaped reward functions. The effectiveness and scalability of the proposed model are upgraded through deep learning. A mean-field approximation deals with environment instabilities, and hierarchical learning distinguishes high-level and low-level decisions. We evaluate our model using various numerical examples based on real data from ShareNow in Berlin, Germany. We show that the policies learned using our decentralized and dynamic approach outperform central static charging strategies. Finally, we conduct a sensitivity analysis for different fleet characteristics to demonstrate the proposed model's robustness and provide managerial insights into the impacts of strategic decisions on fleet performance and derived charging policies.
Item Type: | Journal Article | ||||||||||||||||||||
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URN: | urn:nbn:de:hbz:38-681792 | ||||||||||||||||||||
DOI: | 10.1287/trsc.2022.1187 | ||||||||||||||||||||
Journal or Publication Title: | Transportation science | ||||||||||||||||||||
Volume: | 57 | ||||||||||||||||||||
Number: | 3 | ||||||||||||||||||||
Page Range: | pp. 613-630 | ||||||||||||||||||||
Date: | 2022 | ||||||||||||||||||||
Publisher: | INFORMS | ||||||||||||||||||||
Place of Publication: | CATONSVILLE | ||||||||||||||||||||
ISSN: | 1526-5447 | ||||||||||||||||||||
Language: | English | ||||||||||||||||||||
Faculty: | Faculty of Management, Economy and Social Sciences | ||||||||||||||||||||
Divisions: | Center of Excellence C-SEB Faculty of Management, Economics and Social Sciences > Business Administration > Supply Chain Management > Professur für Digital Supply Chain Managment |
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Subjects: | Economics | ||||||||||||||||||||
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Refereed: | Yes | ||||||||||||||||||||
URI: | http://kups.ub.uni-koeln.de/id/eprint/68179 |
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