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An Integrated AI-Multiple Criteria Decision-Making Framework to Improve Sustainable Energy Planning in Manufacturing Systems: A Case Study

In: Handbook of Smart Energy Systems

Author

Listed:
  • Aida Esmaeilidouki

    (University of British Columbia)

  • Bryn J. Crawford

    (University of British Columbia)

  • Amir Ardestani-Jaafari

    (University of British Columbia)

  • Abbas S. Milani

    (University of British Columbia)

Abstract

Energy planning has historically been a challenging task in sustainable development due to the involvement of multiple criteria, such as social, economic, and environmental impacts (EIs). Multiple criteria decision-making (MCDM) methods have, therefore, attracted much attention to address this challenge. While there have been several opportunities to apply artificial intelligence (AI) and machine learning (ML) algorithms to enable the model to deal with the new situations in solving real-world problems, these methods have not yet been significantly explored in the area of sustainable energy planning. This article develops an insight into the integration of AI with simulation, MCDM technique, and life cycle assessment (LCA) in sustainable energy planning and prospects in this area. An extensive review in this has been performed, and a manufacturing system case study has been developed to illustrate the application of the hybrid proposed framework to improve sustainable energy planning.

Suggested Citation

  • Aida Esmaeilidouki & Bryn J. Crawford & Amir Ardestani-Jaafari & Abbas S. Milani, 2023. "An Integrated AI-Multiple Criteria Decision-Making Framework to Improve Sustainable Energy Planning in Manufacturing Systems: A Case Study," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 2453-2471, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_17
    DOI: 10.1007/978-3-030-97940-9_17
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