Data Envelopment Analyses
Hossein Azizi
Abstract
Purpose: The Analytic Hierarchy Process (AHP) is a multiple criteria decision-making method extensively used in various fields. Prioritization of decision criteria or alternatives from pairwise comparison matrices in AHP has been studied extensively. This article proposed the “Double-Frontier DEA” ...
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Purpose: The Analytic Hierarchy Process (AHP) is a multiple criteria decision-making method extensively used in various fields. Prioritization of decision criteria or alternatives from pairwise comparison matrices in AHP has been studied extensively. This article proposed the “Double-Frontier DEA” approach for prioritization in AHP. This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not.Methodology: One of these methods is Data Envelopment Analysis (DEA). The combination of DEA and AHP (DEAHP) is used to obtain and aggregate weights in AHP. Studies show that DEAHP fails in obtaining and aggregating weights in AHP and sometimes produces priority vectors contrary to evidence for inconsistent pairwise comparison matrices that limits its application.Findings: This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not. Some numerical examples, including a real application of AHP for selecting an innovation team for a university, are provided to specify the advantages of the proposed approach and its potential applications.Originality/Value: The double-frontier DEA approach generates true weights for fully consistent pairwise comparison matrices and best local priorities for inconsistent pairwise comparison matrices, that are logical and fit subjective judgments of decision-makers.
Data Envelopment Analyses
Hossein Azizi
Abstract
Research has revealed that Data Envelopment Analysis (DEA) is an excellent method of data-based performance analysis for comparing decision-making units with multiple inputs and outputs. Selecting inputs and outputs (performance measures) in DEA is a delicate task. In principle, including ...
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Research has revealed that Data Envelopment Analysis (DEA) is an excellent method of data-based performance analysis for comparing decision-making units with multiple inputs and outputs. Selecting inputs and outputs (performance measures) in DEA is a delicate task. In principle, including a large number of inputs and outputs is a positive advantage. However, the inclusion of multiple inputs and outputs might translate into a great deal more of additional data being included, and this may lead to some decision-making units being considered and designated as efficient simply because of their high performance in relation to a number of redundant and useless variables. Elsewhere, in some situations, some performance measures can play both an input and output role. These performance measures are called flexible measures or dual-role factors. Even though models have been developed for working with such dual-role factors, this paper proposes performance appraisal from both an optimistic and pessimistic perspective for selecting a third-party reverse logistics provider in the presence of multiple dual-role factors. A numerical example illustrates the application of the proposed approach.