An Economic Mathematical Fuzzy Model for Data Envelopment Analysis

Abstract

Performance assessment is a central to the management process in any type of organization. In addition, making rational economical decisions to improve organizational performance is a daunting task, as any organization is typically a multi-faceted entity which rely on complex systems that use uncertain information. Data envelopment analysis (DEA) is a powerful quantitative tool that makes use of multiple inputs and outputs to obtain useful information about the performance and efficiency of an organization. In many real-life applications, observations are usually fuzzy in nature. Therefore, DEA efficiency measurement may be sensitive to such variations. The purpose of this study is to develop a unified economical fuzzy DEA model that handles variables of different natures (vague and deterministic) independently and can be adapted to both input- and output-oriented problems, whether it is constant/variable return to scale. To handle fuzzy variables specially the economic variables in the model, the -cut approach was adopted. The model implementation is demonstrated through an illustrative case study. Managers will be able to use this model to identify and remedy underperformance, as well as to design regulations that aim to encourage efficiency and ensure that consumers benefit from the resulting efficiency gains.