An integrated MEREC-taxonomy methodology using T-spherical fuzzy information: An application in smart farming decision analytics

Abstract

This research presents an effective approach for multiple criteria decision analytics by integrating the MEthod based on the Removal Effects of Criteria (MEREC) and the taxonomy technique within the context of T-spherical fuzzy (T-SF) uncertainties. Firstly, a specialized score function tailored for T-spherical fuzziness is developed to enhance methodologies in managing uncertainty within decision-making processes. The T-SF MEREC methodology is then introduced, utilizing this score function to ascertain the objective importance of criteria in uncertain settings. Additionally, the taxonomy methodology is adapted to address decision-analytic challenges associated with T-spherical fuzziness, leveraging T-SF Minkowski distance measures and T-SF weighted averaging and geometric interaction operations. The study also formulates an integrated MEREC-taxonomy methodology to address complex decision-making challenges under T-SF uncertainty. To demonstrate practical utility, these methodologies are applied to smart farming decision analytics. Evaluating various operational models of smart farms in urban agriculture across multiple criteria, the study validates the effectiveness and applicability of the integrated techniques. This successful application underscores the robustness and versatility of the approach, affirming its capacity to enhance decision-making in complex and unpredictable situations.