Usnig LR-Fuzzy Numbers Data to Measure the E ciency and the Malmquist Productivity Index in Data Envelopment Analysis , and Its Application in Insurance Organizations

F. Hosseinzadeh Lot a, A. A. Noora b, H. Nikoomaram c, M. Alimardani d , M. Modi e (a) Dept. of Math. Science and Research Branch, Islamic Azad University, Tehran,Iran. (b) Dept. of Math. Sistan and Balochestan University, Iran, (c) Dept. of Management. Science and Research Branch,Islamic Azad University, Tehran,Iran. (d) Dept. of Math. Tehran -North Branch,Islamic Azad University, Tehran,Iran, (e) Dept. of Math. Science and Research Branch,Islamic Azad University, Zahedan, Iran. ||||||||||||||||||||||||||||||||Abstract In many real applications, the data of production processes can't be precisely measured. We develop some fuzzy versions of the classical DEA models (in particular, the CCR model) by using some ranking methods based on the comparison of cuts. Our approaches can be seen as an extension of the DEA methodology. The provides users and practitioners with models which represent some real life processes more appropriately. DEAbased Malmquist productivity index measures the productivity change over time. In this paper we provide an extension to the DEAbased Malmquist productivity index for all DMUs with fuzzy data.