Priority ranking for the best-worst method

The best-worst method is a recently developed approach that addresses pairwise comparisons in multi-criteria decision-making problems. It has attracted the interest of many academics and has gained widespread use for its effectiveness in reducing the pairwise comparison times and for its strong performance in maintaining consistency between judgments. However, the existing best-worst method prioritization methods don't consider the indirect judgments and fail to meet several established criteria. This paper first develops two prioritization methods, the approximate eigenvalue method and the logarithmic least squares method, to the best-worst method. Then, the inconsistency thresholds of the proposed prioritization methods are given. Finally, the Monte Carlo simulation method is applied to generate random matrixes to compare the performance of various prioritization methods on the selected criteria and analyze the relationship between different consistency measures. It is found that the logarithmic least squares method is the best prioritization method because of simple calculation, considering indirect judgments and containing minimum violations.

Automated summary

The best-worst method is a recently developed approach for pairwise comparisons in multi-criteria decision-making. It is widely used due to its effectiveness in reducing comparison time and maintaining consistency. However, existing prioritization methods for the best-worst method fail to consider indirect judgments and meet established criteria. This paper introduces two new prioritization methods, the approximate eigenvalue method and the logarithmic least squares method, and evaluates their performance using Monte Carlo simulation, concluding that the logarithmic least squares method is the best due to its simplicity, consideration of indirect judgments, and minimal violations.