Analytic hierarchy process and data envelopment analysis: A match made in heaven

Analytic hierarchy process (AHP) and data envelopment analysis (DEA) are two popular decision science methods with many business, science, and engineering applications. AHP is a Multi-Attribute Decision-Making (MADM) method for prioritizing alternatives, and DEA is a non-parametric method for estimating production frontiers. Each method has known strengths and weaknesses, and the strengths in one method can overcome the weaknesses in the other. We study several strategies to diminish the weaknesses of DEA with the strengths of pairwise comparisons in AHP. We tackle the low discrimination power inherent to conventional DEA methods. AHP, with its pairwise comparison capability, has been consistently used to increase the discrimination power and accuracy in DEA. We propose and evaluate several new hybrid MADM-DEA models of different computa-tional complexity and consistency, including combinations of the best-worst method (BWM) and its variants with DEA as well as a novel method composed of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and DEA. We further develop a new technique for evaluating the similarity among multiple ranking results in MADM. The new simple but powerful technique is called Rank Absolute Deviation (RAD) and is inspired by the mean absolute deviation method. Several numerical examples and a real-world problem are used to demonstrate the applicability and efficacy of the new BWM-DEA, MACBETH-DEA, and RAD methods proposed in this study. We illustrate how less computationally demanding MADM-DEA techniques provide rankings that are highly correlated with the benchmark DEA-AHP and different consensus ranking models.

Automated summary

Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) are decision science methods used in business, science, and engineering. AHP is used for prioritizing alternatives, while DEA estimates production frontiers. This study proposes hybrid MADM-DEA models that combine the strengths of AHP with DEA to address the weaknesses of conventional DEA methods and improve discrimination power. The effectiveness of the proposed models is demonstrated through numerical examples and a real-world problem, showing high correlation with benchmark models and consensus rankings.