International Journal of Industrial Engineering and Management

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Vol. 14 No. 4 (2023)
Review Article

Using Index Function and Artificial Intelligence to assess Sustainability: A Bibliometric analysis

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  • Mónica Cardoso,
  • Enrique Ares,
  • Luís Pinto Ferreira,
  • Gustavo Pelaez
Mónica Cardoso
ISEP, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
Enrique Ares
Uvigo— Department of Engineering Design, School of Industrial Engineering, University of Vigo, Vigo,36310, Pontevedra, Spain
Luís Pinto Ferreira
INEGI - Associate Laboratory for Energy, Transports and Aerospace, R. Dr. Roberto Frias 400, 4200-465 Porto, Portugal
Gustavo Pelaez
Uvigo— Department of Engineering Design, School of Industrial Engineering, University of Vigo, Vigo,36310, Pontevedra, Spain
DOI: https://doi.org/10.24867/IJIEM-2023-4-341

Published 2023-12-06

abstract views: 85 // FULL TEXT ARTICLE (PDF): 1


Keywords

  • Sustainability Index Function,
  • Sustainability Manufacturing,
  • Triple Bottom Line,
  • AI Sustainable Manufacturing

How to Cite

Cardoso, M., Ares, E., Pinto Ferreira, L., & Pelaez, G. (2023). Using Index Function and Artificial Intelligence to assess Sustainability: A Bibliometric analysis. International Journal of Industrial Engineering and Management, 14(4), 311–325. https://doi.org/10.24867/IJIEM-2023-4-341

Copyright (c) 2023 International Journal of Industrial Engineering and Management

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Industrial sustainability has increased, focusing on social, economic, and environmental. The aim of this study is to carry out a systematic bibliometric analysis of the existing literature on sustainability indices and artificial intelligence-based tools for measuring and assessing the sustainability of production systems. For this statistical analysis, three scientific databases - Web of Science, Elsevier's Scopus and Dimensions - were used to identify relevant research articles and explore their temporal and geographical distribution. This data sheds light on the development of sustainable manufacturing and encourages future research in this area. This research aims to increase knowledge about sustainable development.

Article history: Received (May 4, 2023); Revised (September 14, 2023); Accepted (October 16, 2023); Published online (October 23, 2023)

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