Decision Algorithm for q-Rung Orthopair Fuzzy Information Based on Schweizer-Sklar Aggregation Operators With Applications in Agricultural Systems

Abstract

The multi-attribute decision-making (MADM) technique is a dominant process for resolving genuine real-life applications and investigating an ideal solution by considering appropriate criteria or attributes. The operational laws of Schweizer-Sklar t-norms and t-conorms are more feasible aggregation operators to serve this purpose. The prioritized aggregation operators also capture single-term aggregated information from given evidence or collected data. In this article, we explore the theory of the q-rung orthopair fuzzy (q-ROF) information to handle awkward and uncertain information of human opinion. Motivated by the significance of the Schweizer-Sklar t-norms and prioritized aggregation operators, we derive a family of mathematical approaches for q-rung orthopair fuzzy information, including q-rung orthopair fuzzy Schweizer-Sklar prioritized average (q-ROFSSPA), q-rung orthopair fuzzy Schweizer-Sklar prioritized weighted average (q-ROFSSPWA), q-rung orthopair fuzzy Schweizer-Sklar prioritized geometric (q-ROFSSPG) and q-rung orthopair fuzzy Schweizer-Sklar prioritized weighted geometric (q-ROFSSPWG) operators. Some notable properties and characteristics are also explored to show the applicability of developed approaches. An application for improving the economic growth of the agriculture sector and a decision algorithm is also discussed under the q-rung orthopair fuzzy environment. With the help of invented mathematical approaches, we resolved a numerical example to choose a suitable crop under reliable characteristics or attributes. To show the reliability and applicability of initiated methodologies, we demonstrate a comparison technique to contrast the results of pioneered aggregation operators with prevailing strategies in the literature.