Multi-agent simulation-Adaptive dynamic programming based reinforcement learning for evaluating joint delivery systems in relation to the different locations of urban consolidation centres
This research applied Multi-agent Simulation-Adaptive Dynamic Programming based Reinforcement Learning (MAS-ADP based RL) to evaluate Joint Delivery Systems (JDS) in relation to different locations of Urban Consolidation Centres in Yokohama, Japan. The MAS-ADP based RL is capable of replicating the potential actions of modeled agents in an uncertain environment accurately. The application of MAS-ADP based RL in this research shows that the location of UCC influences the behavior of freight carriers and the UCC operator in the JDS. Therefore, it is essential to conduct a feasibility study of UCC location to support JDS using MAS-ADP based RL to get better evaluation outcomes.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
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Supplemental Notes:
- © 2020 Nailah Firdausiyah et al. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Firdausiyah, Nailah
- Taniguchi, Eiichi
- Qureshi, Ali Gul
- Publication Date: 2020
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 125-132
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Serial:
- Transportation Research Procedia
- Volume: 46
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2352-1465
- Serial URL: http://www.sciencedirect.com/science/journal/23521465/
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Delivery service; Location; Logistics; Programming (Planning); Urban goods movement
- Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01741642
- Record Type: Publication
- Files: TRIS
- Created Date: May 31 2020 5:57PM