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An Extension of Root Assessment Method (RAM) Under Spherical Fuzzy Framework for Optimal Selection of Electricity Production Technologies Toward Sustainability: A Case Study
Faculty
Computer Science
Year:
2024
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohammed Abdel Basset Metwally Attia
Staff Zu Site
Abstract In Staff Site
Journal:
International Journal of Energy Research willy
Volume:
Keywords :
, Extension , Root Assessment Method (RAM) Under
Abstract:
This paper integrated the root assessment method (RAM) with a spherical fuzzy set (SFS) to rank the alternatives and select the best electricity production technology. The SF-Entropy is used to compute the factor’s weights, and the SF-RAM method ranks the alternatives. This study used four main factors, 29 subfactors, and 24 alternatives. The four main factors are economic, environmental, social, and political. The results show that economic factors are most important, followed by social, environmental, and political factors. The results of the SF-RAM method show that hydropower and wind onshore are the highest-rank technologies in Egypt. The sensitivity and comparative analysis are conducted in this study. The sensitivity analysis was conducted with 30 scenarios in changing factors’ weights and three scenarios in changing of expert’s weight. The sensitivity analysis results show the alternatives’ rank is stable under different scenarios. The comparative analysis was conducted using various multicriteria decision-making (MCDM) methods. The results of the comparative analysis demonstrate that the proposed methodology outperforms other MCDM approaches. This study supports governments in mitigating environmental impacts, ultimately leading to lower morbidity rates.
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Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015
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Saber Mohamed, "An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems", IEEE, 2013
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Saber Mohamed, "Differential Evolution with Dynamic Parameters Selection for Optimization Problems", IEEE, 2014
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