An overview of portfolio optimization using fuzzy data envelopment analysis models

Journal Title: Journal of Fuzzy Extension & Applications - Year 2020, Vol 1, Issue 3


A combination of projects, assets, programs, and other components put together in a set is called a portfolio. Arranging these components helps to facilitate the efficient management of the set and subsequently leads to achieving the strategic goals. Generally, the components of the portfolio are quantifiable and measurable which makes it possible for management to manage, prioritize, and measure different portfolios. In recent years, the portfolio in various sectors of economics, management, industry, and especially project management has been widely applied and numerous researches have been done based on mathematical models to choose the best portfolio. Among the various mathematical models, the application of data envelopment analysis models due to the unique features as well as the capability of ranking and evaluating performances has been taken by some researchers into account. In this regard, several articles have been written on selecting the best portfolio in various fields, including selecting the best stocks portfolio, selecting the best projects, portfolio of manufactured products, portfolio of patents, selecting the portfolio of assets and liabilities, etc. After presenting the Markowitz mean-variance model for portfolio optimization, these pieces of research have witnessed significant changes. Moreover, after the presentation of the fuzzy set theory by Professor Lotfizadeh, despite the ambiguities in the selection of multiple portfolios, a wide range of applications in portfolio optimization was created by combining mathematical models of portfolio optimization.

Authors and Affiliations

Mehrdad Rasoulzadeh; Mohammad Fallah


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  • EP ID EP692657
  • DOI 10.22105/JFEA.2020.255034.1027
  • Views 86
  • Downloads 0

How To Cite

Mehrdad Rasoulzadeh; Mohammad Fallah (2020). An overview of portfolio optimization using fuzzy data envelopment analysis models. Journal of Fuzzy Extension & Applications, 1(3), -.