Track: Optimization

Abstract

In a competitive environment, it is crucial for organizations to know how efficiently and effectively they are operating compared to similar organizations. The challenge is to somehow draw helpful insights from all these numbers that will lead to improvements in the performance of the organization. Efficiency measurement is one aspect of organizational performance. Data Envelopment Analysis (DEA) technique is considered the most appropriate tool for evaluating the performance of a set of comparable homogenous organizations under some predefined conditions. In real-life problems, values for input and/or output variables include uncertainty, this uncertainty may be randomness or vagueness in nature. The purpose of this study is the using of some theoretical results to develop a unified DEA model to handle different uncertainty types, the developed model allows various natures of variables (vagueness, randomness and deterministic) depending on the nature of uncertainty in the variables. Implementation of the model was presented through some cases to illustrate the model functionality. In addition, the results are compared with three other different DEA models; a Combined fuzzy/deterministic model, a Combined stochastic/deterministic model, and a deterministic model. Managers can rely on the developed model to assess relative efficiency in business complex systems associated with different uncertainty natures.