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.