文献类型：期刊文献

英文题名：A robust group decision-making framework for assessing industrial information platforms under uncertainty

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：203

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:001854450300001)、、EI(收录号：20263421329192)、Scopus(收录号：2-s2.0-105047539483)、WOS

基金：The author sincerely thanks the editor and the anonymous reviewers for their valuable time, insightful comments, and constructive suggestions, which have significantly contributed to improving the quality and clarity of this manuscript. The author also gratefully acknowledges the financial support provided by the National Key R&D Program of China (Grant No. 2024YFC3308004) , Research Startup Project of Guangdong Ocean University (Grant No. 060302102607) .

语种：英文

外文关键词：Industrial information platform; Quality with periodicity; Complex intuitionistic fuzzy number; Inversion number; Normalized projection measure

外文摘要：Evaluating periodically upgraded systems like Industrial Information Platforms (IIPs) requires robust methods to synthesize uncertain, multi-expert judgments. This study develops a novel group decision-making (GDM) framework within a complex intuitionistic fuzzy environment. Methodologically, a median-based data center is proposed for robust consensus anchoring, an inversion-based measure is introduced to objectively quantify ex pert data quality, and a normalized projection measure is developed for reliable alternative ranking. Experimental validation with real-world IIP data demonstrates the framework's superiority: the median center improves expert weight discrimination by 28.2% and reduces the standard deviation of alternative scores by 12.1% compared to a mean-based benchmark, providing more statistically consistent ranking foundations. The inversion measure achieves an 11-fold improvement over an entropy-based method in expert weight allocation, and Monte Carlo simulation confirms the difference is highly significant. The normalized projection enhances ranking stability by 37.5% and offers superior interpretability through its bounded [0,11 range. Comprehensive sensitivity analysis confirms the framework's robustness, with the optimal alternative A2 maintaining its top-ranked position across all 11 attribute weight perturbation scenarios. Systematic comparison with the classical TOPSIS method reveals superior robustness: the proposed method achieves a 25% improvement in minimum separation and maintains its optimal alternative as the top-ranked platform in four out of five perturbation scenarios, while TOPSIS fails to preserve its baseline optimal alternative in all but one scenario. Comparison with the VIKOR method shows consistency in identifying the same optimal alternative (A2), but VIKOR's ranking results are sensitive to the compromise coefficient, whereas the proposed method contains no subjective parameters. A dynamic stability experiment confirms that the final alternative ranking A(2) > A(1) > A(4) > A(3) remains perfectly consistent across a wide range of input parameter variations. The proposed framework provides a mathematically rigorous and highly stable tool for comprehensive system evaluation under uncertainty, with broader implications for service system optimization and decision science.