A stochastic data envelopment analysis model using a common set of weights and the ideal point concept

Abstract

The efficiency scores of the decision making units (DMUs) in conventional data envelopment analysis (DEA) are between zero and one and generally several DMUs result in having efficiency scores of one. These models generally only rank the inefficient DMUs and not the efficient ones. In addition, conventional DEA models assume that inputs and outputs are measured precisely on a ratio scale. However, the observed values of the input and output data in real-life problems are often imprecise. In this paper, we propose a common set of weights (CSW) model for ranking the DMUs with the stochastic data and the ideal point concept. The proposed method minimises the distance between the evaluated DMUs and the ideal DMU. We also present a numerical example to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures.