A Basic Algorithm for Generating Individualized Numerical Scale (BAGINS)
Faran Ahmed, Kemal Kilic

TL;DR
This paper introduces a simple, computationally efficient method for customizing numerical scales for linguistic labels in decision-making, improving consistency over fixed scales in AHP.
Contribution
A novel, easy-to-implement scale individualization method based on compatibility, with new metrics and experimental validation for AHP.
Findings
Scale individualization outperforms fixed scale approaches.
The proposed heuristic is easy to learn and computationally efficient.
Experimental results validate the benefits of the new method.
Abstract
Linguistic labels are effective means of expressing qualitative assessments because they account for the uncertain nature of human preferences. However, to perform computations with linguistic labels, they must first be converted to numbers using a scale function. Within the context of the Analytic Hierarchy Process (AHP), the most popular scale used to represent linguistic labels numerically is the linear 1-9 scale, which was proposed by Saaty. However, this scale has been criticized by several researchers, and various alternatives are proposed in the literature. There is a growing interest in scale individualization rather than relying on a generic fixed scale since the perceptions of the decision maker regarding these linguistic labels are highly subjective. The methods proposed in the literature for scale individualization focus on minimizing the transitivity errors, i.e.,…
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Taxonomy
TopicsMulti-Criteria Decision Making
