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Here, we examine the capability of artificial neural networks (ANNs) in sensitivity analysis of the parameters of efficiency analysis model, namely data envelopment analysis (DEA). We are mainly interested to observe the required change of a group of parameters when another group goes under a managerial change, maintaining the score of the efficiency. In other words, this methodology provides a platform for simulating the level of some parameters against the remaining parameters for generating different scenarios, as being in demand for managers.

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     One of the most reliable indicators of the evaluation of the same units is the use of mathematical programming based method called data envelopment analysis (DEA). DEA measures the efficiency score of a set of homogeneous decision making units (DMUs) based on observed input and output. The DEA method has been added to the literature by integrating Farrell's method in such a way that each evaluation unit has multiple inputs and multiple outputs. With the advancement and evolution of this approach, DEAis now one of the active areas of research in measuring performance and has been dramatically welcomed by world researchers. Charnes, Cooper, and Rhodes (CCR) [1] first proposed DEA method to evaluate the relative efficiency for not-for-profit organizations. So far, many studies and researches have been carried out in various associations and universities around the world about DEA and its applications. The simplicity of understanding and implementing the DEA method, along with its high precision and wide application in various political, cultural, social and economic fields has led many researchers to use this method to achieve their goals. So far, more than 50,000 articles, books, theses and more have been published on DEA theories and applications, calculations and issues.

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Cost efficiency models evaluate the ability of decision-making units (DMUs) to produce current
outputs at minimal cost. In real applications, the observed values of the input-output data and
their corresponding input prices are imprecise and vague. This paper employs a fuzzy data
envelopment analysis (Fuzzy DEA) method to study cost efficiency of DMUs. In previous studies
on the cost efficiency, no attention has been paid to the issue of ranking problem in fuzzy
environment. In addition, adequate accuracy is ignored in regards to appropriate range of fuzzy
cost efficiency scores. In this study, the proposed method is applied to assess fuzzy cost efficiency
in accordance with the  -level based approach. In this method, data information is considered
as triangular fuzzy numbers. The main idea is to convert the fuzzy DEA model into a family of
parametric crisp models to estimate the lower and upper bounds of the a-cut of the membership
functions of the cost efficiency measures. Moreover, the problem of ranking DMUs is investigated
based on the fuzzy cost efficiency, using a new method. Finally, the proposed method is illustrated
applying a numerical example, and then comparisons between the proposed method and previous
approaches are carried out.
 

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In this paper, two non-linear technologies are proposed based on weak disposability definitions: weak disposability with non-uniform abatement factors and new weak disposability. Both technologies are applied to Spanish airport systems and the existing technologies are modified. To remove the computational complexity of non-linear approaches, the linearization methods are proposed. Then, in order to evaluate the efficiency measure of decision making units (DMUs), a directional distance function (DDF) is applied to the linear technologies and the analysis of the results is presented.

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Today, new technologies have changed the global financial panorama and communities. Due to the advancement of new technologies and the competitiveness of the company, the nature of innovation has changed. In order to take full advantage of the potential of technological transformation, companies must incorporate platforms into their operations. The purpose of this have a look at became to designing and explaining the model of technological platforms capabilities within the cosmetics enterprise. The prevailing take a look at is carried out in terms of motive and descriptive in phrases of the way to acquire information. The information evaluation method locations this study within the discipline of qualitative research of interpretive type. The study population in the present study consists of all university professors in the field of business management and information technology management. In order to design the model of the present study, interviews were conducted based on purposive and theoretical sampling methods to the extent of theoretical saturation. In this study, in order to evaluate the validity of the interview from the approach of credibility or credibility criteria including the use of negative case strategies, triangulation, rich explanation and reliability approach including the use of third parties and also repetition of the coding process based on the validity model. Qualitative research by Lincoln and Guba (1982) was used. In evaluating the reliability of the interview, two strategies of using third party as well as repetition of coding were used (Lincoln and Guba, 1985). The analysis of the interview data using the data-based method was based on the systematic approach of Strauss and Corbin (1998), based on three stages of open, axial and selective coding. Data coding showed the extraction of 16 selected codes, 60 axial codes and 248 open codes which were classified based on causal conditions, central phenomenon, interfering factors, contextual factors, strategies and consequences.

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Cost efficiency in which cost coefficients are given for some inputs (cost coefficients can be different for disparate decision-making units (DMUs)) is one of the most important concepts in data envelopment analysis (DEA) to analyze the performance. Moreover, in some occasions, the cost performance and changes of input measures should be addressed while the convexity property is violated. Therefore, in this paper, first a DEA model is provided to assess cost efficiency based on the free disposal hull (FDH) model. Then, by considering cost and technical efficiencies achieved, a multi-objective problem called the inverse FDH cost model is presented to determine input values based on output changes while the cost and technical efficiency levels are preserved. The multi-objective problem is computed applying two approaches. Also, a dataset from the literature is presented to show the performance of the proposed method. For this purpose, we used the data of six banks in different countries. We added 2% to the outputs and analyzed the inputs with two models. In the first model, we used cost coefficients for weights, and in the second model, we used the same weights. Contrary to forecasts, some entries have decreased and others have increased. But from the results, we have noticed that the first model is more realistic because most of the solutions have increased in this model.
 

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One of the most useful tools in Operations Research (OR) which is essentially applied to evaluate the performance of treated Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). Because of in the current decades, DEA models have been used and extended in many disciplines and hence attracted much interests. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. Since, in the real situations, input data are included some uncertainties, hence in this study we consider a DEA with fuzzy stochastic data and suggest a three-stage DEA model by taking into account undesirable output. To achieve this aim, an extended probability approach is applied to the reform of three-stage DEA models. Finally, we give an illustrative example by considering a case study on the banking industry.

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Tone [29] proposed a method of super-efficiency slack-based measures (SBM) for ranking efficient decision-making units (DMUs), so that this model would rank efficient DMUs. The established model was able to measure radially. It calculates and measuring the efficiency of inefficient DMUs and the amount of super-efficiency of efficient DMUs. Du et al. [11] developed the Charens et al. [6] model in to the additive DEA model, as well as the additive super performance model. Turn et al. [32] used a linear SBM and S-SBM integrated model that had the properties of both models and reduced the time factor compared to previous models. In order to be able to calculate the amount of additive super efficiency; First we identify the efficient DMUs and then apply the additive super-efficiency model to the efficient DMUs. In this paper, the proposed model obtains the additive efficiency value of inefficient DMUs and the additive super efficiency value of efficient DMUs with less computation time. The amount of DMUs calculated from the integrated model in this article can be compared to the Guo et al. [15] article in comparison with the time table of the text of the article.
 

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Supply chains often have different technologies. Additionally, organizations with multiple stages can evaluate their operational efficiency by analyzing scale elasticity, which helps determine if they are functioning optimally or if there is room for improvement. This evaluation allows for the identification of potential inefficiencies and opportunities for enhancement. Consequently, this research introduces a two-stage DEA-based approach with undesirable outputs to examine the scale elasticity of supply chains within meta and group frontiers. The measurement of group and meta performance of general systems and stages is conducted for this purpose. Moreover, the study addresses the scale elasticity of supply chains with undesirable outputs by considering the heterogeneity of technologies. To achieve this, the study focuses on the right and left scale elasticity of efficient general systems and each stage. A real-world application from the soft drink industry is provided to illustrate the proposed model. The results show the applicability of the introduced methodology.