A new approach to re-evaluating consistency in the analytic hierarchy process (AHP) using simulated consistent matrices is presented. The proposed consistency evaluation method makes use of statistically significant deviations from the average consistency measure for the simulated matrices. This addresses most of the deficiencies of the conventional consistency ratio (CR) method. A pairwise comparison matrix (PCM) is adjudged inconsistent by the proposed method if its consistency measure exceeds the modeled consistency threshold. Comparison of the consistency evaluation for simulated nearly-consistent matrices using the proposed method shows a statistically significant reduction of the order-specific bias in comparison with the CR method. The proportion of nearly consistent matrices which are evaluated as ‘inconsistent’ increases more than three-folds when the evaluation is done using the CR method. Several examples are presented which illustrate the advantages of the proposed method and differences in classification with the CR approach. Evaluation of consistency using the proposed method of statistically derived thresholds from simulated, nearly consistent matrices is more nuanced and objective, as well as intuitive in its interpretability.

中文翻译：

---

基于模拟一致性矩阵的层次分析法一致性再评价

提出了一种使用模拟一致矩阵重新评估层次分析法 (AHP) 中的一致性的新方法。所提出的一致性评估方法利用了与模拟矩阵的平均一致性度量的统计显着偏差。这解决了传统一致性比率 (CR) 方法的大部分缺陷。如果成对比较矩阵 (PCM) 的一致性度量超过建模的一致性阈值，则该方法将判断其不一致。使用所提出的方法对模拟的近似一致矩阵进行一致性评估的比较表明，与 CR 方法相比，订单特定偏差在统计上显着降低。当使用 CR 方法进行评估时，被评估为“不一致”的几乎一致矩阵的比例增加了三倍以上。给出了几个例子来说明所提出的方法的优点以及与 CR 方法的分类差异。使用所提出的从模拟的、几乎一致的矩阵中统计导出阈值的方法来评估一致性更加细致和客观，并且在可解释性方面也更加直观。