Int. J. Industrial and Systems Engineering, Vol. 29, No. 1, 2018 19 Copyright © 2018 Inderscience Enterprises Ltd. A novel hybrid approach based on fuzzy DEA-AHP Ramezan Nemati Keshteli Faculty of Engineering – East Guilan, University of Guilan, Rasht, Iran Email: r.nemati@guilan.ac.ir Owais Torabi*, Eesa Mahmoudi and Hassan Ghasemi Department of Noor, Iran University of Science and Technology, Tehran, Iran Fax: +981333690609 Email: o_torabi@noor.iust.ac.ir Email: eesa_mahmoudi@noor.iust.ac.ir Email: hassan_ghasemi @ noor.iust.ac.ir *Corresponding author Abstract: The data envelopment analysis (DEA) model is a non-parametric programming technique that helps to efficiency evaluating for every decision making units (DMUs) with multiple inputs and multiple outputs. In traditional DEA model, we need crisp data. But in the real world, most of the data are imprecise and uncertain. A major cause of uncertainty related to the non-quantifiable, incomplete and unachievable information. For this reason, fuzzy logic and fuzzy sets developed in different models of DEA. In this paper, a new hybrid model is developed for performance evaluation problems ranking of DMUs, with multiple inputs and fuzzy outputs. To achieve the best ranking of DMUs, fuzzy analytical hierarchy process (FAHP) is applied. Solving FAHP ends up in a full ranking of DMUs. Finally, to illustrate the proposed model an example is presented. Keywords: fuzzy data envelopment analysis; FDEA; efficiency analysis; fuzzy analytical hierarchy process; FAHP. Reference to this paper should be made as follows: Keshteli, R.N., Torabi, O., Mahmoudi, E. and Ghasemi, H. (2018) ‘A novel hybrid approach based on fuzzy DEA-AHP’, Int. J. Industrial and Systems Engineering, Vol. 29, No. 1, pp.19–30. Biographical notes: Ramezan Nemati Keshteli holds a BS and MS in Industrial Engineering from Khajeh Nasir University of Technology, Iran, and Isfahan University of Technology, Iran, respectively, and a PhD in Industrial Engineering from Tarbiat Modares University, Tehran, Iran. His research interests include statistical quality control and artificial intelligence.