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Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework

Author

Listed:
  • Pedro Ponce

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico)

  • Citlaly Pérez

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico)

  • Aminah Robinson Fayek

    (Hole School of Construction Engineering, Department of Civil and Environmental Engineering, University of Alberta, 7-203 Donadeo Innovation Centre for Engineering, 9211 116 St NW, Edmonton, AB T6G 1H9, Canada)

  • Arturo Molina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico)

Abstract

The demand for electrical energy has increased since the population of and automation in factories have grown. The manufacturing industry has been growing dramatically due to the fast-changing market, so electrical energy for manufacturing processes has increased. As a result, solar energy has been installed to supply electrical energy. Thus, assessing a solar panel company could be a complex task for manufacturing companies that need to assess, install, and operate solar panels when several criteria with different hierarchies from decision-makers are involved. In addition, the stages of a solar panel system could be divided into analysis, installation, operation, and disposal, and all of them must be considered. Thus, the solar panel company must provide a holistic solution for each stage of the solar panel lifespan. This paper provides a fuzzy decision-making approach (Fuzzy TOPSIS) to deal with the assessment of solar companies using the S4 framework in which the sensing, smart, sustainable, and social features are labeled with linguistic values that allow the evaluation of companies using fuzzy values and linguistic labels, instead of using crisp values that are difficult to define when decision-makers are evaluating a solar company for installation of the solar panels. The S4 features are considered the benefits of the evaluation. In the case study presented, three solar panel companies with different alternatives are evaluated on the basis of three decision-makers from manufacturing companies using the S4 framework. This paper considers the benefits of solar companies in the context of decision-makers participating in a multi-decision selection of such a company to install solar panels, so that the selection process is more effective. Thus, the proposed Fuzzy TOPSIS method proved efficient when selecting a solar panel company from among many options that best meets the needs of manufacturing companies.

Suggested Citation

  • Pedro Ponce & Citlaly Pérez & Aminah Robinson Fayek & Arturo Molina, 2022. "Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8838-:d:981853
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    References listed on IDEAS

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    1. Citlaly Pérez & Pedro Ponce & Alan Meier & Lourdes Dorantes & Jorge Omar Sandoval & Javier Palma & Arturo Molina, 2022. "S4 Framework for the Integration of Solar Energy Systems in Small and Medium-Sized Manufacturing Companies in Mexico," Energies, MDPI, vol. 15(19), pages 1-28, September.
    2. Chia-Nan Wang & Van Thanh Nguyen & Hoang Tuyet Nhi Thai & Duy Hung Duong, 2018. "Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam," Energies, MDPI, vol. 11(6), pages 1-27, June.
    3. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    4. Zhongdong Yu & Wei Liu & Liming Chen & Serkan Eti & Hasan Dinçer & Serhat Yüksel, 2019. "The Effects of Electricity Production on Industrial Development and Sustainable Economic Growth: A VAR Analysis for BRICS Countries," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    5. Aleksandra Bączkiewicz & Bartłomiej Kizielewicz & Andrii Shekhovtsov & Mykhailo Yelmikheiev & Volodymyr Kozlov & Wojciech Sałabun, 2021. "Comparative Analysis of Solar Panels with Determination of Local Significance Levels of Criteria Using the MCDM Methods Resistant to the Rank Reversal Phenomenon," Energies, MDPI, vol. 14(18), pages 1-21, September.
    6. Aouam, T. & Chang, S. I. & Lee, E. S., 2003. "Fuzzy MADM: An outranking method," European Journal of Operational Research, Elsevier, vol. 145(2), pages 317-328, March.
    7. Li, Tao & Li, Ang & Guo, Xiaopeng, 2020. "The sustainable development-oriented development and utilization of renewable energy industry——A comprehensive analysis of MCDM methods," Energy, Elsevier, vol. 212(C).
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    Cited by:

    1. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    2. Paweł Kut & Katarzyna Pietrucha-Urbanik, 2024. "Bibliometric Analysis of Multi-Criteria Decision-Making (MCDM) Methods in Environmental and Energy Engineering Using CiteSpace Software: Identification of Key Research Trends and Patterns of Internati," Energies, MDPI, vol. 17(16), pages 1-27, August.
    3. José de Jesús Camacho & Bernabé Aguirre & Pedro Ponce & Brian Anthony & Arturo Molina, 2024. "Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review," Energies, MDPI, vol. 17(2), pages 1-32, January.

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