MTA-PE "Lendület" Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.
Department of Quantitative Methods, Faculty of Business and Economics, University of Pannonia, Veszprém, Hungary.
PLoS One. 2022 Feb 25;17(2):e0264277. doi: 10.1371/journal.pone.0264277. eCollection 2022.
The Promethee-GAIA method is a multicriteria decision support technique that defines the aggregated ranks of multiple criteria and visualizes them based on Principal Component Analysis (PCA). In the case of numerous criteria, the PCA biplot-based visualization do not perceive how a criterion influences the decision problem. The central question is how the Promethee-GAIA-based decision-making process can be improved to gain more interpretable results that reveal more characteristic inner relationships between the criteria. To improve the Promethee-GAIA method, we suggest three techniques that eliminate redundant criteria as well as clearly outline, which criterion belongs to which factor and explore the similarities between criteria. These methods are the following: A) Principal factoring with rotation and communality analysis (P-PFA), B) the integration of Sparse PCA into the Promethee II method (P-sPCA), and C) the Sum of Ranking Differences method (P-SRD). The suggested methods are presented through an I4.0+ dataset that measures the Industry 4.0 readiness of NUTS 2-classified regions. The proposed methods are useful tools for handling multicriteria ranking problems, if the number of criteria is numerous.
Promethee-GAIA 方法是一种多准则决策支持技术，它定义了多个准则的综合等级，并基于主成分分析（PCA）对其进行可视化。在存在大量准则的情况下，基于 PCA 双标图的可视化方法无法感知准则如何影响决策问题。核心问题是如何改进基于 Promethee-GAIA 的决策过程，以获得更具可解释性的结果，揭示准则之间更具特征的内在关系。为了改进 Promethee-GAIA 方法，我们提出了三种技术，这些技术可以消除冗余的准则，并清楚地说明哪个准则属于哪个因子，并探索准则之间的相似性。这些方法如下：A）具有旋转和共性分析的主成分分析（P-PFA），B）稀疏 PCA 与 Promethee II 方法的集成（P-sPCA），C）排序差异总和方法（P-SRD）。所提出的方法通过一个 I4.0+数据集呈现，该数据集衡量了 NUTS 2 分类区域的工业 4.0 准备情况。如果准则数量众多，这些方法是处理多准则排名问题的有用工具。
