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An Agent-Based Training System for Optimizing the Layout of AFVs' Initial Filling Stations

Author

Listed:
  • Tieju Ma
  • Jiangjiang Zhao
  • Shijian Xiang
  • Ya Zhu
  • Peipei Liu

Abstract

The availability of refuelling locations for alternative fuel vehicles (AFVs) is an important factor that drivers consider before adopting an AFV; thus, the layout of initial filling stations for AFVs will influence the adoption of AFVs. This paper presents a training system for optimising the layout of initial filling stations for AFVs by linking an agent-based model of the adoption of AFVs with a real city/area's road network, as well as the city/area's social and economic background. In the agent-based model, two types of agents (driver agents and station owner agents) interact with each other in a city/area's road network, stored in a GIS (Geographic Information System). With simulation scenario analyses and a genetic algorithm, the training system presented in this paper can help decision makers determine close-to-optimal layouts for initial AFV filling stations. This paper also presents a case study of the application of the training system that analyses the layout of fast-charging or battery-changing stations for the promotion of electric vehicles adoption in Shanghai.

Suggested Citation

  • Tieju Ma & Jiangjiang Zhao & Shijian Xiang & Ya Zhu & Peipei Liu, 2014. "An Agent-Based Training System for Optimizing the Layout of AFVs' Initial Filling Stations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-6.
    • Handle: RePEc:jas:jasssj:2013-127-3
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    Citations

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    Cited by:

    1. Zhao, Jiangjiang & Ma, Tieju, 2016. "Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations," European Journal of Operational Research, Elsevier, vol. 249(2), pages 706-716.
    2. Arijit Ghosh & Neha Ghorui & Sankar Prasad Mondal & Suchitra Kumari & Biraj Kanti Mondal & Aditya Das & Mahananda Sen Gupta, 2021. "Application of Hexagonal Fuzzy MCDM Methodology for Site Selection of Electric Vehicle Charging Station," Mathematics, MDPI, vol. 9(4), pages 1-27, February.
    3. Yi, Zonggen & Bauer, Peter H., 2016. "Optimization models for placement of an energy-aware electric vehicle charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 227-244.
    4. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.

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