Spherical Fuzzy Information Aggregation Based on Aczel-Alsina Operations and Data Analysis for Supply Chain

Abstract

Spherical fuzzy sets (SFSs) are often made up of membership, nonmembership, and hesitancy grades, and also have the advantage of accurately representing decision makers (DMs) preferences. This article proposes novel spherical fuzzy aggregation operators (AOs) based on Aczel-Alsina (AA) operations, which offer a lot of advantages when tackling real-world situations. We begin by introducing some new SFS operations, such as the Aczel-Alsina product, the Aczel-Alsina sum, the Aczel-Alsina exponent, and the Aczel-Alsina scalar multiplication. We developed many AOs namely, the "spherical fuzzy Aczel-Alsina weighted averaging (SFAAWA) operator," "spherical fuzzy Aczel-Alsina ordered weighted averaging (SFAAOWA) operator," "spherical fuzzy Aczel-Alsina hybrid averaging (SFAAHA) operator," "spherical fuzzy Aczel-Alsina weighted geometric (SFAAWG) operator," "spherical fuzzy Aczel-Alsina ordered weighted geometric (SFAAOWG) operator," and "spherical fuzzy Aczel-Alsina hybrid geometric (SFAAHG) operator." Different attributes of these operators have been defined. The idempotency, boundary, monotonicity, and commutativity of suggested averaging and geometric operators are demonstrated. Then, based on these operators, we propose a novel approach for tackling the "multi-criteria decision-making" (MCDM) problems. We use a agriculture land selection scenario to demonstrate the efficacy of our proposed approach. The outcome confirms the new technique's applicability and viability. Furthermore, sensitivity analysis and a comparison analysis between the existing approaches and the recommended technique have been provided.