Window Data Envelopment Analysis (WDEA) is a popular, effective, and applicable methods for dynamic performance assessment of peer decision making units (DMUs). WDEA is a non-parametric panel method that operates based on the principle of moving averages and establishes efficiency measures by treating each DMU in different periods as a separate DMU. By applying the WDEA approach, a decisionmaker (DM) can measure the efficiency of different DMUs in different periods through a sequence of overlapping windows. Also, WDEA can increase the discrimination power by increasing the number of DMUs when a limited number of DMUs is available. Given the advantages of the WDEA approach and its applications in realworld problems, this paper surveys and analyses 387 WDEA papers published from 1985 to 2020. The paper also recommends some suggestions, guidelines, and opportunities for future research. Notably, the findings show the applicability and efficacy of WDEA in the literature.

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The main goal of this paper is to propose interval network data envelopment analysis (INDEA) model for performance evaluation of network decision making units (DMUs) with two stage network structure under data uncertainty. It should be explained that for dealing with uncertainty of data, an interval programming method as a popular uncertainty programming approach is applied. Also, to show the applicability of proposed model, IN-DEA approach is implemented for performance measurement and ranking of 10 insurance companies from Iranian insurance industry. Note that insurance companies are undoubtedly one of the most important pillars of the financial markets, whose great performance will drive the economy of the country. The empirical results indicate that the proposed INDEA is capable to be utilized to assess the performance of two-stage DMUs in the presence of interval data.

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

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