MCGDM Using Generalized-Spherical Fuzzy VIKOR Technique for Sustainable and Optimal Foundation Crop Seed Selection in Agriculture

Haridas Mondal ¹ ( )
Totan Garai ² ( )
Tipu Sultan Haque ³ ( )
Shariful Alam ⁴ ( )

Submited date : 2025-02-11 Accepted date : 2025-08-14

Keywords: Multi-criteria group decision making (MCGDM), Generalized-spherical fuzzy (GSF), Agricultural breeder seed selection, GSF-VIKOR MCDM methodology.,

Abstract :

In recent years, the VIKOR Multi-Criteria Decision-Making (MCDM) methodology has become one of the most popular techniques in the field of decision making, used to address various real-life problems. In this article, we extend this MCDM technique under the Generalized Spherical Fuzzy (GSF) realm, which provides an effective and robust framework for handling real-life uncertainty. Nowadays, sustainable agricultural practices have become essential for ensuring long-term food security, particularly in the context of increasing population pressures and growing environmental challenges. Therefore, this article aims to select the optimum high-yielding foundation crop seeds using the GSF-VIKOR MCDM technique, through dealing with real-world uncertainties and conflicting decision factors. A novel score and accuracy function have been proposed and employed with the MCDM technique in a fully neutrosophic approach to effectively manage real-life uncertainties encountered in the decision-making process. Several aggregation operators are used to handle and consolidate large data sets. The GSF-Dombi Weighted Arithmetic Aggregation(GSF-DWAA), GSF-Dombi Weighted Geometric Aggregation(GSF-DWGA), GSF-WAM, and GSF-WGM operators are incorporated into the selection process to properly manage the decision expert ratings for the decision-making. Additionally, the validity and superiority of the extended method have been verified and compared with the well-established TOPSIS MCDM techniques. The experiments have been conducted with the help of a constructed data set due to the lack of a real data set. The sensitivity and compatibility of the proposed technique have subsequently been investigated. Moreover, the advantages and limitations of the proposed method have been highlighted. Finally, the robustness of the technique has been investigated for large sets of uncertain data, and the reliability of outcomes has been compared with other existing methodologies.

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