Solution of Data Envelopment Analysis Model by Fuzzy Mathematical Programming Approach

Abstract

In many real applications, the data of production processes cannot be precisely measure. This Is Particularly worrying when assessing efficiency with frontier-type models, such as data Envelopment analysis (DEA) models, they are very sensitive to possible data errors. For this reason, the possibility of having available a methodology that allows the analyst to deal with imprecise data becomes an issue of great interest in these contexts. Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the inputs and outputs by evaluating their relative efficiency. It represents a set of linear programming techniques and uses deterministic data (inputs and outputs), in stable conditions. An efficient integration of production and distribution plans into a unified framework is critical to achieving competitive advantage. In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values of the input and output data in real-life problems are sometimes imprecise or vague. This paper finds efficiency measures with fuzzy inputs and outputs via proposed model. However, a real supply chain operates in a highly dynamic and uncertain environment. From the theory point of view, the objective of this study is to develop a simple and effective Fuzzy DEA model. This paper proposes an interactive evaluation process for measuring the relative efficiencies in fuzzy DEA with consideration of the preferences. it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. Our approaches can be seen as an extension of the DEA methodology that provides users and practitioners with models which represent some real life processes more appropriately.