No abstract

One of the important topics in Data Envelopment Analysis is congestion. Many scholars research in this field and represent their methods. In most of the represented methods, we have to solve lots of models or its used for a special aim like negative data, integer data, different Production Possibility Set and etc. Here we represent our method that measures the congestion without solving a model. It can be used for different Production Possibility Set (different technology) like and ; different data like negative data and integer data. Also, we can distinguish strongly or weakly congestion of Decision Making Unit. Furthermore, each DMU has congestion, efficient and inefficient, we can measure it by this method. Finally, we represent some numerical example of our purpose method and compare our method with other methods then show the results in tables.

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. For this, three steps are considered. In the first step, the stock companies screen by their financial data. For second step, we need some inputs and outputs for solving Data Envelopment Analysis (DEA) models. Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βVaR (MShβV) model and the Multi Objective MeanSharp-βVaR (MOMShβV) model based on Range Directional Measure (RDM) that can take positive and negative values. Also, we consider one of downside risk measures named Value at Risk (VaR) and try to decrease it. After using our proposed models, the efficient stock companies will be selected for making the portfolio. In the third step, Multi Objective Decision Making (MODM) model was used to specify the capital allocation to the stock companies that was selected for the portfolio. Finally, a numerical example of the purposed method in Iranian stock companies is presented.