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عضو هیئت علمی دانشگاه آزاد اسلامی واحد علوم و تحقیقات

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عضو هیئات مدیره و خزانه دار انجمن ایرانی تحلیل پوششی داده ها

دارای بیش از 300 مقاله معتبر علمی بین المللی

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دانشگاه علوم و تحقیقات یکی از بزرگ‌ترین دانشگاه‌های مستقل کشور می‌باشد و این دانشگاه  بعد از دانشگاه تهران در رتبه دوم نسبت به کل دانشگاه‌های دولتی (سراسری) و غیردولتی کشور قرار گرفته‌است که تا انتهای سال ۱۴۰۱ شمسی، این دانشگاه بیشتر از ۴۴ هزار مقاله منتشر نموده‌است و از این جهت دانشگاه علوم و تحقیقات تهران در تولید علم در کشور با همه امکانات و توان خود سهیم بوده‌است.

موضوعات تحقیقاتی:

مدل سازی در زمینه تصمیم سازی
کاربرد نظریه فازی در تصمیم گیری
تحلیل پوششی داده ها
کاربرد نظریه بازی ها در تصمیم گیری
طراحی سیستم های پشتیبانی تصمیم گیری
کاربرد ریاضیات در مالی و بورس​
بهینه سازی خطی و غیرخطی
تصمیم گیری چند معیاره

مقالات:

Fuzzy data envelopment analysis: An adjustable approach
Presents a novel fuzzy DEA based on a general fuzzy measure, Develops an adjustable and flexible fuzzy DEA model to consider DMUs’ preferences, Applying the adjustable FDEA model for measuring efficiency of hospitals in USAPejman Peykani , Emran Mohammadi , Ali Emrouznejad , Mir Saman Pishvaee , Mohsen Rostamy-Malkhalifeh
Presents a novel fuzzy DEA based on a general fuzzy measure, Develops an adjustable and flexible fuzzy DEA model to consider DMUs’ preferences, Applying the adjustable FDEA model for measuring efficiency of hospitals in USA
Data envelopment analysis and robust optimization: A review
This paper reviews the milestone approaches for handling uncertainty in data envelopment analysis (DEA). This paper presents the detailed classifications of robust data envelopment analysis (RDEA). RDEA is appropriate for measuring the efficiencies of decision-making units in the presence of the data and distributional uncertainties. This paper reviews scenario-based and uncertainty set of DEA models. It covers 73 studies from 2008 to 2019. The paper concludes with suggestions about the guidelines for future researches in the field of RDEAPejman Peykani, Emran Mohammadi, Reza Farzipoor Saen, Seyed Jafar Sadjadi, Mohsen Rostamy-Malkhalifeh
This paper reviews the milestone approaches for handling uncertainty in data envelopment analysis (DEA). This paper presents the detailed classifications of robust data envelopment analysis (RDEA). RDEA is appropriate for measuring the efficiencies of decision-making units in the presence of the data and distributional uncertainties. This paper reviews scenario-based and uncertainty set of DEA models. It covers 73 studies from 2008 to 2019. The paper concludes with suggestions about the guidelines for future researches in the field of RDEA
A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange
Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks’ efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain dataPejman Peykani,Emran Mohammadi ,Armin Jabbarzadeh,Mohsen Rostamy-Malkhalifeh,Mir Saman Pishvaee
Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks’ efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data
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