# A novel aggregation framework based on complex n,m-rung orthopair fuzzy aczel-alsina operators for renewable energy decision-making

## TL;DR

This paper introduces a new decision-making framework using fuzzy logic to improve renewable energy selection by handling uncertainty and ambiguity effectively.

## Contribution

The novel Cn,m-ROFAAWA and Cn,m-ROFAAWG operators combine complex n,m-rung orthopair fuzzy sets with Aczel-Alsina aggregation for MADM.

## Key findings

- Wind Energy consistently ranks highest in renewable energy selection using the proposed framework.
- The new operators outperform existing methods in distinguishing alternatives and improving decision precision.
- Smaller parameter values enhance differentiation among alternatives, ensuring practical applicability.

## Abstract

This paper develops an advanced decision-making framework using complex n,m-rung orthopair fuzzy (Cn,m-ROF) sets combined with aczel-alsina aggregation operations to effectively manage uncertainty and ambiguity in multiple attribute decision-making (MADM). Two novel aggregation operators—Cn,m-ROFAAWA (weighted average) and Cn,m-ROFAAWG (weighted geometric)—are formulated and examined for their theoretical properties, such as boundedness, idempotency, and monotonicity. The framework is demonstrated through a renewable energy selection case study, where numerical results indicate that Wind Energy consistently ranks highest across varying parameter settings, highlighting the reliability and stability of the proposed approach. Comparative evaluations reveal that the suggested operators outperform existing methods in distinguishing among alternatives and enhancing decision precision. Analysis of parameter influence shows that while larger parameter values increase alternative scores, the optimal choice remains unchanged, confirming isotonicity. Sensitivity assessments further indicate that smaller parameter values improve differentiation among alternatives, ensuring practical applicability. The study underscores the effectiveness of integrating complex n,m-rung orthopair fuzziness with aczel-alsina operations for MADM, and suggests future extensions to other fuzzy information frameworks to enhance applicability and flexibility.