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Attribute reduction approaches under interval-valued q-rung orthopair fuzzy soft framework

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Abstract

The interval-valued q-rung orthopair fuzzy sets and soft sets are two different uncertainty theories to cope with incomplete and uncertain information in several real-world multi-attribute decision-making (MADM) situations. This study develops a novel hybrid model called interval-valued q-rung orthopair fuzzy soft sets (IVqROFSSs, for brevity) to generalize the interval-valued q-rung orthopair fuzzy set model and to address the decision-makers preference information more effectively in complicated MADM processes. Afterward, some basic useful properties of the proposed model are explored, including subset relation, complement, union, intersection, the ‘AND’ operation, and the ‘OR’ operation. Further, four kinds of attribute reduction techniques for IVqROFSSs are presented. An algorithm for each reduction approach is developed and explained through an illustrative numerical example which verifies that developed reduction methods remove the redundant attributes by preserving the ranking order of decision objects unchanged. Later on, an application is proposed, that is, site selection for a wind power plant, to explain the developed model’s reliability and its reduction approaches. Finally, the proposed hybrid model and its attribute reduction methods are compared with some existing models, and their attribute reduction approaches, respectively.

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Authors and Affiliations

  1. Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan

    Ghous Ali & Muhammad Afzal

  2. School of Mathematics, Xiamen University, Xiamen, China

    Muhammad Asif

  3. Department of Computer Science, National Textile University, Faisalabad, Pakistan

    Adeel Shazad

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  1. Ghous Ali
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  2. Muhammad Afzal
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  3. Muhammad Asif
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  4. Adeel Shazad
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Correspondence to Ghous Ali.

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Ali, G., Afzal, M., Asif, M. et al. Attribute reduction approaches under interval-valued q-rung orthopair fuzzy soft framework. Appl Intell 52, 8975–9000 (2022). https://doi.org/10.1007/s10489-021-02853-x

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