International Journal of Data Envelopment Analysis
Volume:8 Issue: 2, May 2020

The current study extends and provides a generalization of the range directional model (RDM). The proposed generalized RDM (GRDM) model utilizes an ideal point (IP) and anti-ideal point (AIP) simultaneously to evaluate the efficiency score. It is evident that approaching to the IP, not necessarily, leads to moving away from the AIP. This obviously happens when the IP, AIP, and DMU lie on a common line; however, the depicted situation usually occurs hardly ever. On the other side, there are loads of situations in which a DMU requires not only to approach the IP but also to move away from the AIP, simultaneously. The GRDM model imposes two criteria (i.e., approaching to IP and moving away from AIP) to asses DMUs. Therefore, the efficiency score, when GRDM is used, is less than or equal to the efficiency score obtained by using the RDM model; consequently, the discrimination power of the GRDM model is better than that of the RDM model due to finding more inefficiency regarding both IP and AIP. The GRDM model is unit- and translation-invariant. A numerical example is applied to demonstrate the applicability of the proposed model in comparison with the RDM model.

Insurance industry is one of the most important factors for the economic development of the countries/ For example, insurance industry can be important for the stability of financial systems mainly because they are large investors in financial markets, because there are growing links between insurers and banks and because insurers are safeguarding the financial stability of households and firms by insuring their risks/ This paper focuses on the efficiency evaluation of the insurance industry/ For this purpose, we uses the dataset of the car insurance policies of Saman Insurance Company during the years 2018-2019 and implements an extended cross efficiency method to rank the insured for prediction the risk of insurers in terms of existence of damage risk or absence of damage risk

Hospitals, as the biggest and costliest operative units of ministry of health and medical education, have always faced budget deficit. Hence, efficiency scores of hospitals is one of the important criteria that managers and policy makers can use for future planning to improve the performance of the hospitals. This paper presents data envelopment analysis (DEA) to assess relative efficiency of hospitals with multiple inputs and multiple outputs. We use cross-efficiency score for ranking the top hospitals and also Malmquist productivity index for estimating productivity growth. This study evaluates the efficiency of hospitals operated by Kashan University of Medical Sciences from 2011 to 2016, in which input parameters are the number of physicians, nurses and beds and output parameters are the number of discharged patients. GAMS software application was used for data analysis. Based on the results, the average technical efficiency of understudy hospitals was 0.71. On the other hand, inefficient hospitals faced input increase to achieve the same output. The average productivity index at hospitals during study years was 0.909, indicating that the productivity index reduced on average 10% during this time range. Hospitals affiliated with Kashan University of Medical Sciences were technically inefficient. Thus, at these hospitals, technical, pure technical and scale efficiency did not follow a fixed trend, changing continually. Moreover, hospitals did not use their resources optimally and encountered decrease in productivity. Therefore, it is recommended that hospitals’ functionality be compared with national and international standards.

Working capital indicators can be one of the most influential indicators in companies' financial decision-making. Therefore, research in this field can be useful. The main purpose of this study is to measure efficiency by considering working capital management indicators with the help of super-efficient data envelopment analysis models. Many researchers, including Goal et al. (2014), have used current measurements of the efficiency of working capital management indicators, despite the shortcomings of the cash conversion cycle, specifically in the cash conversion cycle. In this study, according to their idea, 21 companies active in the Iran Insurance Industry Exchange have been evaluated over a 5-year period. Periodic review can reveal information about firms' performance fluctuations as well as the relationship between changes in their rankings and changes in working capital indicators. Since this assessment is based on working capital indicators, the results will provide better opportunities for business managers, shareholders and investors to make large and partial decisions. Finally, the above method of data envelopment analysis will be compared with traditional methods.

The identification of undesirable congestion is important to avoid a cost increase and a shortage of generation. However, the identification of desirable congestion or eco-technology innovation, in systems that produce undesirable outputs is much more important than that of undesirable congestion from the perspective of environmental assessment. In this paper, we propose an approach to identify desirable congestion that can be effectively used to reduce the amount of undesirable outputs so that systems such as electric power companies satisfy a governmental standard on environmental protection. Thus, the identification of desirable congestion assists us in determining which technology should be invested to facilitate eco-technology innovation and its related engineering management for a future sustainable economic growth. We use the proposed approach to study the pollutants of two empirical data in China and USA.

Data Envelopment Analysis is a nonparametric method based on the mathematical model. The concept of return to scale is one of the most important issues in economics and also in the data envelopment analysis, which includes a large part of the studies. Determining the type of return to scale (increasing, decreasing, and constant) will provide information to the manager through which he will be able to decide on how to achieve the optimal unit level under evaluation. Despite the fact that in the majority of the studies the concept of return to scale (RTS) has been investigated in radial models, this paper, in order to recognize the type of return to scale, has expressed and proved a method based on a non-radial additive model. We also developed this method for a two-stage network and in addition to inputs and outputs; we have introduced new entry intermediate measures in the intermediate products to the system. Then, we estimate and prove the type of return to scale for this network model. At the end, examples are given to examine the proposed method.