详细信息
An inversion-based group decision-making method for evaluating industrial information platforms ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:An inversion-based group decision-making method for evaluating industrial information platforms
作者:Yue, Chuan[1]
机构:[1]Guangdong Ocean Univ, Coll Math & Comp Sci, Haida Rd, Zhanjiang 524088, Guangdong, Peoples R China
年份:2025
卷号:47
外文期刊名:JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
收录:SCI-EXPANDED(收录号:WOS:001513754500001)、、EI(收录号:20252518620280)、Scopus(收录号:2-s2.0-105008143516)、WOS
语种:英文
外文关键词:Industrial information platform; Extended VIKOR method; Median; Inversion number; Group regret matrix; Group satisfaction matrix
外文摘要:Quality evaluation of industrial information platforms represents a typical multi-dimensional decision-making problem that requires comprehensive integration of multi-stakeholder perspectives. This paper proposes a novel group decision-making evaluation framework with two key innovations: (1) The introduction of the inversion number concept from linear algebra to quantify evaluators' data quality, combined with median statistics to establish a dynamic weight allocation mechanism for decision-makers; (2) Building upon the traditional VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods' group utility measure, this work innovatively incorporates group regret and group satisfaction matrices, constructing a tripartite "utility-regret-satisfaction" evaluation system through a normalized projection technology, thereby forming an extended VIKOR decision architecture. The proposed method's feasibility and practicality are validated through a case study on industrial information platform assessment. Experiments demonstrate that: (i) Different data centers can lead to distinct decision outcomes; (ii) Different measures can lead to different decision outcomes; (iii) The inversion-based data quality metric outperforms entropy-based alternatives (with 10% accuracy improvement); (iv) Alternative rankings maintain 70%-100% stability intervals. This research provides a quantifiable, highly robust theoretical tool for multi-attributes decision-making in complex industrial systems.
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