Saeedeh Ketabi

Work place: Department of Management, Faculty of Administrative Sciences and economics, University of Isfahan, IRAN



Research Interests: Medicine & Healthcare


Dr. Saeedeh Ketabi is professor of Operation Research (OR) in Management Department, University of Isfahan, IRAN.  She received her B.S in Applied Mathematics from Tehran University in 1987 and her Master in Operation Research from Isfahan University of Technology Isfahan, IRAN. She also received her PHD from University of Adelaide, Australia. She possesses an experience of more than 20 years in the field of OR teaching and research. She has published nearly 50 papers in international and national journals and conferences. She especially interested in Operations Research applications in health care.

Author Articles
Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

By Bahram Izadi Bahram Ranjbarian Saeedeh Ketabi Faria Nassiri-Mofakham

DOI:, Pub. Date: 8 Sep. 2013

Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis have recently attracted the researchers’ interest. This study evaluates multi-group discriminant linear programming (MDLP) for classification problems against well-known methods such as neural networks, support vector machine, and so on. MDLP is less complex compared to other methods and does not suffer from local optima. However, sometimes classification becomes infeasible due to insufficient data in databases such as in the case of an Internet Service Provider (ISP) small and medium-sized market considered in this research. This study proposes a fuzzy Delphi method to select and gather required data. The results show that the performance of MDLP is better than other methods with respect to correct classification, at least for small and medium-sized datasets.

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Developing a Virtual Group Decision Support System Based on Fuzzy Hybrid MCDM Approach

By Bahram Izadi Saeedeh Ketabi

DOI:, Pub. Date: 8 Jan. 2013

Organizational decisions involve with unusually vague and conflicting criteria. This controversy increases empirical uncertainties, disputes, and the resulting consequences of these decisions. One possible method in subduing this problem is to apply quantitative approaches to provide a transparent process for resolute conclusions which enables decision makers to formulate accurate and decisive on time decisions. Although numerous methods are presented in the literature, the majority of them aim to develop theoretical models. However, this article aims to develop and implement an integrated fuzzy virtual MCDM model based on fuzzy AHP and fuzzy TOPSIS as a decision support system (DDS). Preventing disadvantageous face-to-face decision-making by achieving positive benefit from virtual decision making causes the proposed DDS to be suitable for making crucial decisions such as supplier selection, employee selection, employee appraisal, R&D project selection, etc. The proposed DDS has been implemented in an optical company in Iran.

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