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Research Article | Open Access

Efficient picture fuzzy soft CRITIC-CoCoSo framework for supplier selection under uncertainties in Industry 4.0

Ayesha Razzaq1Muhammad Riaz1Muhammad Aslam2( )
Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Department of Mathematics, College of Sciences, King Khalid University, Abha 61413, Saudi Arabia
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Abstract

The picture fuzzy soft set (PiFSS) is a new hybrid model to address complex and uncertain information in Industry 4.0. Topological structure on PiFSS develops an innovative approach for topological data analysis to seek an optimal and unanimous decision in decision-making processes. This conception combines the advantages of a picture fuzzy set (PiFS) and a soft set (SS), allowing for a more comprehensive representation of the ambiguity in the supplier selection. Moreover, the criteria importance through intercriteria correlation (CRITIC) and the combined compromise solution (CoCoSo) technique is applied to the proposed framework to determine the relative importance of the evaluation parameter and to select the most suitable supplier in the context of sustainable development. The suggested technique was implemented and evaluated by applying it to a manufacturing company as a case study. The outcomes reveal that the approach is practical, efficient and produces favorable results when used for decision-making purposes. Evaluating and ranking of efficient suppliers based on their sustainability performance can be effectively accomplished through the use of PiFS-topology, thus facilitating the decision-making process in the CE and Industry 4.0 era.

CLC number: 03E72, 90B50, 94D05

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AIMS Mathematics
Pages 665-701

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Cite this article:
Razzaq A, Riaz M, Aslam M. Efficient picture fuzzy soft CRITIC-CoCoSo framework for supplier selection under uncertainties in Industry 4.0. AIMS Mathematics, 2024, 9(1): 665-701. https://doi.org/10.3934/math.2024035

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Received: 01 September 2023
Revised: 29 October 2023
Accepted: 07 November 2023
Published: 15 January 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)