A Common Weight Multi-criteria Decision analysis-data Envelopment Analysis Approach with Assurance Region for Weight Derivation from Pairwise Comparison Matrices

Deriving weights from a pairwise comparison matrix (PCM) is a subject for which a wide range of methods have been presented. This paper proposes a common weight multi criteria decision analysis-data envelopment analysis (MCDA-DEA) approach with assurance region for weight derivation from a PCM. The proposed method has a more discrimination power over the conventional methods for weight derivation from a pairwise comparison matrix. Furthermore, the proposed model has several merits over the competing approaches and lacks the drawbacks of the well-known Data Envelopment Analysis HP and data envelopment analysis / assurance region methods (DEA/AR) methods. Some numerical examples and a case study are taken from the literature in order to confirm the merits of the proposed method and its applications in multi criteria decision making. Results disclose the advantages of the proposed approach.

A Common Weight Multi-criteria Decision analysis-data Envelopment Analysis Approach with Assurance Region for Weight Derivation from Pairwise Comparison Matrices Keywords:

Analytic Hierarchy Process , Data Envelopment Analysis , Data Envelopment Analysis HP , Common WeightsAssurance Region