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Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

Author

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  • R. Krishankumar

    (School of Computing, SASTRA University, Thanjavur-613401, TN, India)

  • K. S. Ravichandran

    (School of Computing, SASTRA University, Thanjavur-613401, TN, India)

  • Samarjit Kar

    (Department of Mathematics, National Institute of Technology, Durgapur 713209, TN, India)

  • Fausto Cavallaro

    (Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy)

  • Edmundas Kazimieras Zavadskas

    (Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio al. 11, Vilnius LT-10223, Lithuania)

  • Abbas Mardani

    (Department of Marketing, College of Business Administration, University of South Florida, Tampa, FL 33813, USA)

Abstract

As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.

Suggested Citation

  • R. Krishankumar & K. S. Ravichandran & Samarjit Kar & Fausto Cavallaro & Edmundas Kazimieras Zavadskas & Abbas Mardani, 2019. "Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4202-:d:254579
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    References listed on IDEAS

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    1. Seung Hyo Baek & Byung Hee Lee, 2019. "Optimal Decision-Making of Renewable Energy Systems in Buildings in the Early Design Stage," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
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    4. Cavallaro, Fausto & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Mardani, Abbas, 2019. "Assessment of concentrated solar power (CSP) technologies based on a modified intuitionistic fuzzy topsis and trigonometric entropy weights," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 258-270.
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