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Analysis of Robot Selection Based on 2-Tuple Picture Fuzzy Linguistic Aggregation Operators

Author

Listed:
  • Arshad Ahmad Khan

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China)

  • Muhammad Qiyas

    (Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan)

  • Saleem Abdullah

    (Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan)

  • Jianchao Luo

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China)

  • Mahwish Bano

    (Department of Mathematics, Air University, Islamabad 44000, Pakistan)

Abstract

The aim of this article is to propose the 2-tuple picture fuzzy linguistic aggregation operators and a decision-making model to deal with uncertainties in the form of 2-tuple picture fuzzy linguistic sets; 2-tuple picture fuzzy linguistic operators have more flexibility than general fuzzy set. We proposed a number of aggregation operators, namely, 2-TPFLWA, 2-TPFLOWA, 2-TPFLHA, 2-TPFLWG, 2-TPFLOWG, and 2-TPFLHG operators. The distinguished feature of the developed operators are studied. At that point, we used these operators to design a model to deal with multiple attribute decision-making issues under the 2-tuple picture fuzzy linguistic information. Then, a practical application of robot selection by manufacturing unit is given to prove the introduced technique and to show its practicability and effectiveness. Besides this, a systematic comparison analysis with other existent approaches is conducted to reveal the advantage of our developed method. Results indicate that the proposed method is suitable and effective for decision-making problems.

Suggested Citation

  • Arshad Ahmad Khan & Muhammad Qiyas & Saleem Abdullah & Jianchao Luo & Mahwish Bano, 2019. "Analysis of Robot Selection Based on 2-Tuple Picture Fuzzy Linguistic Aggregation Operators," Mathematics, MDPI, vol. 7(10), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:1000-:d:278891
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    References listed on IDEAS

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    2. Guiwu Wei, 2016. "Picture fuzzy cross-entropy for multiple attribute decision making problems," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 17(4), pages 491-502, July.
    3. Wancheng Zhang & Yejun Xu & Huimin Wang, 2016. "A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(2), pages 389-405, January.
    4. Shouzhen Zeng & Muhammad Qiyas & Muhammad Arif & Tariq Mahmood, 2019. "Extended Version of Linguistic Picture Fuzzy TOPSIS Method and Its Applications in Enterprise Resource Planning Systems," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, January.
    5. Herrera, F. & Martinez, L. & Sanchez, P. J., 2005. "Managing non-homogeneous information in group decision making," European Journal of Operational Research, Elsevier, vol. 166(1), pages 115-132, October.
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