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This research aims to introduce generalized fermatean fuzzy soft sets (GFFSSs) to improve decision-making in supply chain management.

July 12, 2026International Journal of Computational Intelligence Systems0 citationsOpen Access

Enhanced TOPSIS, VIKOR and Hamacher Averaging Aggregation Operators on Generalized Fermatean Fuzzy Soft Sets and Their Application in Supply Chain Management

NANadeem Ajaib XYXiaopeng Yang KHKhizar Hayat

Key Points

This research aims to introduce generalized fermatean fuzzy soft sets (GFFSSs) to improve decision-making in supply chain management.
Introduced Generalized Fermatean Fuzzy Soft Hamacher Weighted Averaging and Ordered Weighted Averaging operators.
Detailed application of VIKOR and TOPSIS methods within the framework of GFFSSs.
Conducted a case study on supply chain analyst selection to validate proposed methods.
Demonstrated the robustness of the proposed MCDM techniques through comparative analysis with established methods.
Showed that enhanced aggregation operators significantly improved decision-making quality.
Highlighted sensitivity of results to input variations, underscoring the versatility of GFFSSs in practical applications.

Abstract

Abstract Despite the fact that the Fermatean Fuzzy Soft Sets (FFSSs) take into account both parameters and specific material choices, certain general patterns associated with parameters are often significant in complex Multi-Criteria Decision Making (MCDM) problems. The purpose of this research is to present generalized FFSSs (GFFSSs) by incorporating crucial information along with the attributes of FFSSs at the same time. In GFFSSs the Fermatean Fuzzy Numbers (FFNs) represent a higher space where membership and non-membership are obtained; however, having a suitable knowledge representation framework alongside trustworthy tools, such as aggregation operators, is crucial for effectively addressing uncertainty. This study introduces Generalized Fermatean Fuzzy Soft Hamacher Weighted Averaging (GFFSHWA) and Generalized Fermatean Fuzzy Soft Hamacher Ordered Weighted Averaging (GFFSHOWA) operators along with their properties. On the other hand, MCDM techniques have made substantial use of VIekriterijumsko KOmpromisno Rangiranje (VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in recent decades. VIKOR and TOPSIS approaches for FFSSs use criterion and option information, however, implicit information is usually addressed using integrated techniques in real and complex material choice situations. This study presents an integrated application of the VIKOR and TOPSIS methods in the context of GFFSSs. In supply chain management (SCM), selecting an appropriate supply chain analyst (SCA) is a essential decision making problem for enhancing the efficiency, cost-effectiveness, and trustworthiness of an organization’s supply chain through the collection, analysis, and interpretation of data pertaining to purchase, inventory, production, and distribution. On the basis of the information obtained through GFFSSs, a case study is presented that discusses the selection of an SCA for the purpose of SCM robustness. The discussion on three distinct proposed MCDM approaches is conducted through the conclusive outcomes of the case study. This study demonstrated the robustness and sensitivity of the proposed MCDM methods through a comparative analysis with established techniques.

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