文献类型：期刊文献

英文题名：A novel method for large-scale group decision-making with application to e-commerce software system evaluation

作者：Yue, Chuan[1]

机构：[1]Guangdong Ocean Univ, Coll Math & Comp Sci, Zhanjiang 524088, Guangdong, Peoples R China

年份：2026

卷号：191

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:001703858000001)、、EI(收录号：20260319935853)、WOS

基金：This work was supported by the National Key R&D Program of China under Grant 2024YFC3308004.

语种：英文

外文关键词：Large-scale group decision-making; Golden ratio; Inversion number; Intuitionistic fuzzy number; Software system evaluation

外文摘要：BACKGROUND: Large-scale group decision-making (LSGDM) in big data environments faces challenges in robust data center construction, objective expert weighting, and efficient information fusion. OBJECTIVE: This study aims to develop a novel LSGDM framework integrating a Golden Ratio-based data center and an inversion-based data quality metric to improve ranking stability and decision reliability. METHODS: A GR-based data center was introduced to replace conventional mean/median centers, alongside an inversion-number-driven quality metric for expert weighting and a scalable aggregation technique for converting crisp data into intuitionistic fuzzy matrices. The framework was validated through dynamic experiments and sensitivity analysis. RESULTS: The GR-based center outperformed mean/median centers in 95% of test scenarios. The inversion-based method achieved perfect ranking consistency (Kendall's tau = 1), showing a 50% improvement over entropy-based methods (tau = 2/3), and maintained 100% ranking stability under parameter variations-20 times higher than entropy-based approaches. CONCLUSION: The proposed framework offers a robust, quantitatively validated solution for LSGDM in data-intensive environments, with significant advantages in consistency and scalability.