An inversion-based group decision-making method for evaluating industrial information platforms

Abstract

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.