The Analytic Hierarchy Process (AHP) is one of the most widely used quantitative tools in multi-criteria decision-making problems. Despite its popularity and use due to its simple but systematic procedure, AHP has limitations especially in terms of the numerical comparison scale used in one of its core steps: pairwise comparisons. AHP is based on verbal comparisons of alternatives/criteria, which are, then, converted into quantitative scores with a one-to-one mapping between the verbal comparisons and a predetermined numerical scale. The choice of the numerical scale affects an essential characteristic of pairwise comparisons: consistency. In order to understand the intrinsic consistency propinquities, this study evaluates the most widely used numerical pairwise comparison scale (Fundamental Scale) and other numerical scales that have been proposed since the initial formulation of AHP. After identifying the limitations of known scales, a new scale based on Fibonacci series is developed considering these limitations, and further analysis is conducted through extensive simulations. The results show that the proposed scale performs well when compared to the other scales.

中文翻译：

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用于层次分析过程的基于斐波那契数列的成对比较量表

层次分析法（AHP）是多准则决策问题中使用最广泛的定量工具之一。尽管由于其简单但系统的程序而广受欢迎和使用，但 AHP 具有局限性，特别是在其核心步骤之一中使用的数值比较尺度方面：成对比较。层次分析法基于备选方案/标准的口头比较，然后将其转换为定量分数，并在口头比较和预定的数字尺度之间进行一对一的映射。数值尺度的选择会影响成对比较的一个基本特征：一致性。为了理解内在的一致性相似性，本研究评估最广泛使用的数值成对比较尺度（基本尺度）和自 AHP 最初制定以来提出的其他数值尺度。在确定已知尺度的局限性后，考虑到这些局限性，开发了基于斐波那契数列的新尺度，并通过广泛的模拟进行了进一步分析。结果表明，与其他量表相比，所提出的量表表现良好。