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Multi-attribute decision-making with q-rung picture fuzzy information

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Abstract

q-rung picture fuzzy sets can handle complex fuzzy and impression information by changing a parameter q based on the different hesitation degree and yield a flexible framework that captures imprecise information involving different views (typically but not exclusively: yes, abstention, no, and rejection). The Einstein operators perform well for the aggregation of data in various other frameworks of uncertain information. By combining these concepts, in this article we expand the field of application of the Einstein operators to the q-rung picture fuzzy environment. Thus, we develop novel concepts of q-rung picture fuzzy aggregation operators under Einstein operators and discuss their application in multi-attribute decision-making. First, we propose Einstein operational laws for q-rung picture fuzzy numbers. We then introduce the q-rung picture fuzzy Einstein weighted averaging, q-rung picture fuzzy Einstein ordered weighted averaging, generalized q-rung picture fuzzy Einstein weighted averaging and generalized q-rung picture fuzzy Einstein ordered weighted averaging operators. We develop an algorithm to solve complex decision-making problems using these operators. Finally, to show the practicality and effectiveness of the proposed method, we discuss two multi-attribute decision-making problems (1) selection of a suitable business location (2) selection of a supplier. To demonstrate the superiority and advantage of our proposed method, a comparison with existing methods is presented.

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Authors and Affiliations

  1. Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan

    Muhammad Akram & Gulfam Shahzadi

  2. BORDA Research Unit and IME, University of Salamanca, Salamanca, Spain

    José Carlos R. Alcantud

Authors
  1. Muhammad Akram
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  2. Gulfam Shahzadi
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  3. José Carlos R. Alcantud
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Correspondence to Muhammad Akram.

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Akram, M., Shahzadi, G. & Alcantud, J.C.R. Multi-attribute decision-making with q-rung picture fuzzy information. Granul. Comput. 7, 197–215 (2022). https://doi.org/10.1007/s41066-021-00260-8

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  • DOI: https://doi.org/10.1007/s41066-021-00260-8

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