Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach
Name
processes-13-01120.pdf
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2.91 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Briceño, Citlaly Pérez
Ponce, Pedro
Fayek, Aminah Robinson
Anthony, Brian
Bradley, Russel
Peffer, Therese
Meier, Alan
Mei, Qipei
Date Issued
April 8, 2025
Journal
Processes
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Briceño, C.P.; Ponce, P.; Fayek, A.R.; Anthony, B.; Bradley, R.; Peffer, T.; Meier, A.; Mei, Q. Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach. Processes 2025, 13, 1120.
Version
Final published version
Abstract
The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/pr13041120
