Abstract
Effective decision-making techniques are essentially dependent on the capacity to balance (symmetry) requirements and their fulfilment, that is, the capacity to accurately identify a collection of factors that have the greatest influence on performance. Data envelopment analysis (DEA) is a useful nonparametric method in operations research for performance estimation by measuring the efficiency scores of the decision-making units. In this paper, we develop a global search method (GSM) for selecting the key input and output variables in DEA models. The GSM measures the effects of variables with respect to the efficiency scores directly, i.e., by considering the average change when a variable is added or removed from the analysis. It aims to produce DEA models that include only the key variables with the largest impact on the results. The effectiveness of the GSM is demonstrated using a case study from 15 US banks, with the results analyzed and discussed. The outcomes indicate that the GSM yields useful insight for decision-makers to make informed decisions in undertaking their problems.
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