Akademik Məlumat İdarəetmə Sistemi
Advanced Engineering Informatics, vol.69, 2026 (SCI-Expanded, Scopus)
Evaluating robotic operations in manufacturing is essential for aligning technology with production objectives, yielding improvements in product quality, efficiency, safety, cost, and competitiveness. To address this complex decision-making problem, this study introduces a novel framework based on spherical fuzzy Z-number sets. The main contribution lies in the development of new Schweizer–Sklar-based prioritized averaging and geometric aggregation operators under the spherical fuzzy Z-number environment. These operators are designed to enhance flexibility in information aggregation by integrating Schweizer–Sklar t-norms and t-conorms, thereby overcoming the limitations of existing operators. A key advantage of the proposed approach is its ability to handle biased decision-makers and unbalanced data more effectively than current models. In addition, the paper examines the mathematical properties of the developed operators, ensuring their validity and robustness. To demonstrate practical applicability, a case study on robotic operation quality in manufacturing is conducted. By evaluating multiple robotic systems against key performance criteria, the study identifies PrecisionMax 3000 as the most suitable option for industries requiring high accuracy and precision. Comparative analyses further highlight the effectiveness of the proposed operators. This work contributes a new set of aggregation operators and a structured decision-making framework that can be applied in manufacturing and other complex multi-attribute environments.