International Journal of Data Envelopment Analysis
Volume:5 Issue: 1, Winter 2017

In this paper we have applied Genetic-DEA modelling to help decision makers improve national economic performance through enhancing intellectual property rights indices. We categorized countries applying a novel classification approach and applied genetic algorithm and data envelopment analysis for modelling the relativity of property rights behavior of nations to their economic productivity. We also present a new concept as the uncertainty factor for priority suggestions to have a confidence factor tailored for each specific country for priority recommendations. The results of our research indicate that rich countries shall let people easy access to loans and fight copyright piracy afterwards. Middle income countries have to first enhance the independency of their judicial system and thenceforth respect intellectual property rights. Subsequently, they need to enhance their political stability. Countries that pay few respects to property rights shall boost judicial independence as the first priority and then advance the protection of physical property rights. Poor countries are advised to enhance registering properties and then focus on the rule of law.

Due to strict competition in the global market for Tourism services and Hotels in the tourism industry and also the importance of satisfying tourists, awareness about the efficiency of hotel for hotel owners and hotel managers is very important. The purpose of this paper is measuring performance of hotels in Crete by using Robust Data Envelopment analysis (RDEA) technique considering uncertain data. The proposed method of this paper develops a RDEA method with the consideration of uncertainty on output parameters. In order to use robust optimization methods in this article, after the introduction of input and output, we calculate the efficiency of 50 luxury hotel in Crete by means of GAMS software. The method is based on the adaption of robust optimization approaches proposed in the literature. Finally, we compare the performance achieved with previous research on these hotels and our results. It is found that the efficiency decreases, but the level of confidence increases.

Data Envelopment Analysis (DEA) technique uses linear programming to evaluate the relative efficiency of a homogeneous set of Decision Making Units (DMUs) in their use of multiple inputs to produce multiple outputs. The standard DEA models do not take into account non-discretionary inputs and outputs and ignore the possibility that efficiency may be correlated with the non-discretionary factors. However, one key issue in performance measurement problems is how to treat non-discretionary factors, which influence the performance of DMUs and are, at the same time, out of the control of the management. In this paper, a new model for measuring efficiency is defined such that non-discretionary factors are taken into account by the decision maker. The main contributions of this paper are fourfold: (1) we review the existing approaches for measuring efficiency scores to control non-discretionary factors in production; (2) we provide a discussion of strengths and weaknesses and highlighting potential limitations of the existing non-discretionary DEA models; (3) we propose a new approach based on relative importance of non-discretionary inputs that overcomes existing weaknesses; (4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.

Volatility and uncertainty of the real world is inevitable. Changes in input and output units make the loss of confidence in the results obtained from the performance assessment. To overcome this problem robust optimization suggested.in previous studies, measuring of interval efficiency were calculated based on optimistic viewpoint and pessimistic view point, while we believe that this approach ignores the frequency distribution that could affect ranking of DMUs. In present study, we try using Interval estimation of the mean, to Increase the confidence of efficiency by considering scattered data. At the end, we compare the obtained result of confidence interval DEA and robust DEA (RDEA) ranking in, terms of uncertainty.

This study presents a reasonable program for large commercial banks in order to supply bank resources by the long term bank deposits and investments, making balance between financial commitments and investments, enhancing the value at risk by maintaining market and bank high liquidity, management of crisis in condition of liquidity shortage and funds, assessment of value at risk index by using determination bank interval efficiency, ranking the set of commercial big bank by using of the fuzzy data envelopment analysis (DEA) models. In the following, we extend fuzzy slack-based model (SBM) for fuzzy inputs and outputs data. We are determined the risk factors in bank operating process by using of inefficiency concept. In this study, we use the data of seven banks which were accepted in Tehran Stock Exchange (Eghtesad novin bank, Parsian, Tejarat, Sina, Karafarin, Melat and Saderat) over a 4 years' period from 2012 to 2015. We use the fuzzy DEA for assessment of value at risk index for Banks listed on the Tehran Stock Exchange.

Sensitivity analysis in Data Envelopment Analysis (DEA) is studied for perturbations of data for which ranking of efficient Decision Making Units (DMUs) is preserved. Sufficient conditions for efficient DMUs to preserve their ranks under the perturbations of data are achieved. Accordingly, it can be found out how to change outputs or inputs of an efficient DMU while preserving ranking of all efficient DMUs. In addition, an illustrative numerical example is provided to receive a better comprehension.