محدودیت منابع یک واقعیت غیر قابل انکار است که بسیاری از سازمان‌ها (نظیر سازمان‌های رسانه‌ای) با آن در ارتباط هستند؛ از این رو، بهبود بهره‌وری منابع این سازمان‌ها نیز یکی از مهم‌ترین نگرانی‌های مدیران است. در نتیجه، یک مدیر باید عملکرد سازمان خود را پیوسته ارزیابی کند. در این مقاله، قصد داریم میزان پیشرفت و پسرفت روزنامه‌های چاپی ایران را بین سال‌های (1394) و (1395) ارزیابی کنیم. این مطالعه، از لحاظ هدف کاربردی است و از شاخص بهره‌وری مالم‌کوئیست که یک رویکرد بسیار مفید در تحلیل پوششی داده‌ها برای اندازه‌گیری میزان پیشرفت و پسرفت واحدهای تصمیم‌گیرنده‌ی تحت ارزیابی است، استفاده کرده است. جهت تجزیه و تحلیل داده‌ها نیز از نرم‌افزار گمز که یک سیستم مدل‌سازی قوی برای برنامه‌نویسی ریاضی و بهینه‌سازی است، استفاده شده است. پس از مرور ادبیات ارزیابی عملکرد و مصاحبه با خبرگان حوزه‌ی مدیریت رسانه و همچنین، کارشناسان معاونت مطبوعاتی وزارت فرهنگ و ارشاد اسلامی، شاخص‌های مورد نیاز شناسایی و انتخاب شده است. نتایج حاصل از مقایسه‌ی شاخص‌های درون سازمانی در دو سال متوالی (1394) و (1395) نشان می‌دهد از 87 واحد تصمیم‌گیرنده‌ی تحت ارزیابی، 29 واحد پیشرفت و 58 واحد پسرفت داشته‌اند. به منظور بهبود واحدهایی که پسرفت داشته‌اند، استراتژی مناسب پیشنهاد شده است. Manuscript profile

By developing the new services such as VoIP and Videoconference, using a mechanism is needed to support the quality of service of the application programs. Different models have been presented to guarantee the quality of service. Among these, the differentiated services can be mentioned which was presented by IETF. In the architecture of the differentiated services, no admission control mechanism is considered. To guarantee the quality of service, the differentiated services network should support the admission control mechanism. In order to have the best result of the admission control mechanism, the parameters of the network should be considered to decrease the rate of loss and delay as well as the increase of the network utilization. Therefore, due to the spontaneous evaluation of the efficiency of the different inputs and finding the best set of inputs which produce the best outputs, the Data Envelopment Analysis (DEA) is used. Data Envelopment Analysis is one of the scientific approaches which computes the efficiency using the strong mathematical basis. In this paper, first the parameter based admission control mechanism is added to the edge routers of the differentiated services network and implemented by NS-2 simulator. Then, the best set of inputs is using the DEA. Manuscript profile

In the environment of business‐to‐business e‐commerce, Buyers and sellers in mature industrial markets can turn single transactions into long-term beneficial relationships by a deeper understanding of the complex connection between the two and buyers and sellers are uncertain about their roles. A “must-do” for the sellers, in particular, is to understand patterns of investment and reward, and effectively manage the process that defines the dynamics of buyer-seller evolution. This paper tries to use data envelopment analysis as a reliable and achievable tool for performance evaluating, quality and performance improvement of Buyer-Seller Relationship, in the situation where the information flows are imprecise data between buyers and sellers. Manuscript profile

All managers need to evaluate the units under their supervision. To evaluate the units, they must determine the evaluation indicators and then calculate the efficiency of each unit with the help of these indicators. In practice, many indicators may not be small, but have a qualitative value or their values are fuzzy in this article we want to calculate the units under evaluation that have the same inputs and outputs. To evaluate the performance of these units with fuzzy data, first for each unit, we calculate the nature of the output in such a way that according to the desired input, what can be the maximum value of the output because the number of outputs is more than one, so we have a multi-objective linear programming problem to be answered by solving the multi-objective linear programming problem We use flexible fuzzy. In the introduced model, we first change the fuzzy linear programming problem to a multi-objective linear programming problem with three objective functions using the alphabetical method arranged on triangular fuzzy numbers, so using this method is an optimal solution. Lexicography of the ML problem We find OP. Manuscript profile

Cross-efficiency evaluation in Data envelopment analysis (DEA) has been accepted as a useful tool for performance evaluation and ranking of decision making units. In this paper using Undesirable Multiple Form (UMF) model with specific risk of α, a new stochastic model called Expected Ranking Criterion is introduced using statistical techniques for efficiency evaluation decision making units (DMU). Another issue in applying cross-efficiency DEA models is considering stochastic in input and output variables. Also, the non-uniqueness of optimal weights in this evaluation has reduced the usefulness of this powerful method. As a result, it is recommended that secondary goals be introduced in cross-efficiency evaluation. In this paper, the cross-efficiency model is modified to deal with stochastic data by applying chance-constrained approach. Manuscript profile

Data envelopment analysis (DEA) is a body of research methodologies to evaluate overall efficiencies, identify the sources, and estimate the amounts of inefficiencies in inputs and outputs.efficient DMUs all have an efficiency of one, so that for these units no ranking can be given. Since in evaluating by traditional DEA models many DMUs are classified as efficient, a large number of methods for fully ranking both efficient and inefficient DMUs have been proposed. In the last decade, ranking DEA efficient units has become the interests of many DEA researchers and a variety of models (called super-efficiency models) were developed to rank DEA efficient units. Super efficiency data envelopment analysis model can be used in ranking the performance of efficient DMUs. While the models developed in the past are interesting and meaningful, they have the disadvantages of being infeasible or instable occasionally. But the main problem of super-efficient models is lack of differentiation between non- extreme efficient DMUs, so these models cannot rank these DMUs. In this paper, we propose a new method for Ranking Non-extreme Efficient Decision making units in Data Envelopment Analysis based on benchmark. One of the main advantages of our approach is that, this method doesn’t apply any new models, rather this model applies a combination of the well-known models for ranking DMUs. Therefore, understanding our proposed method is easy for readers. One numerical example is examined to illustrate the potential applications of the proposed method. Manuscript profile

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 Manuscript profile

The studies on congestion, in the field of either economic or operations research, have been grown in recent years. Calculating and determining congestion have two important aspects. By reducing the input in the decision-making unit, which has experienced congestion, we can also reduce its expenses. Moreover, congestion can lead to a reduction in outputs. By its obviation, output can be increased, and in turn, it can also increase the profits consequently. In this research, congestion of decision-making units in data envelopment analysis was estimated in the presence of fuzzy data. In most of the problems of the real and practical world, the obtained information are not exact, even they are fuzzy. Here, the interesting point is how to calculate congestion of data if they are fuzzy. At this point, congestion can be calculated through triangular fuzzy data using in a particular interval. The suggested model provides an interval solution, which can be used as a guide in decisions making. Manuscript profile

This study combines Data Envelopment Analysis (DEA) with machine learning clustering method in datamining for finding the most efficient Decision Making Unit (DMU) and the best clustering algorithm, respectively. The problem of assessment of units by using DEA may not be straightforward due to the data uncertainty. Several scholars have been attracted to develop methods which incorporate uncertainty into input/output values in the DEA literature. On the other hand, in many real world applications, the data is reported in the form of intervals. This means that each input/output value is selected from a symmetric box. In the DEA literature, this type of uncertainty has been addressed as Interval DEA approaches. The main goal of this study is to evaluate the efficiency of banks in the case of data uncertainty with cross-efficiency method in the DEA literature. For this purpose, we consider the BCC-CCR and CCR-BCC models in the presence of uncertain data to find the superior model. After applying the optimization models, in machine learning step, clustering method is applied. Clustering is a procedure for grouping similar items together which this group is called the cluster. Also, the different clustering algorithms can be used according to the behavior of data. In this study, we apply the farthest first and expectation maximization algorithms and show that, in the case of data uncertainty, the BCC-CCR and farthest first algorithms are as a superior optimization model and machine learning algorithm, respectively. Manuscript profile

One of the best methods for computing the efficiency of decision-Making Units (DMU) is Data Envelopment Analysis (DEA) that is useful for improving organizational performance. If we added a new unit to our observation sets, we have to run the model again. Nowadays, datasets from many organizations in the real world have been growing. So, we need a huge amount of computation for examining efficiency for new dataset. To overcome this problem, we combine Machine Learning (ML) and DEA. We consider organizations have interval data. According to we have interval data set, so we use interval DEA. Actually, we link between interval DEA and ML algorithms. First, we compute the efficiency score of these organizations by using Interval DEA. Second, we compute two scores that come in the first stage. Then, use these scores in ML. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 89%. Manuscript profile

The classic models for the performance assessment in Data Envelopment Analysis (DEA) may have some inherent issues. For example, they can be affected by the statistical noise in data. Furthermore, if the decision maker (DM) adds new decision-making units (DMUs) into the evaluation, then the performance of all the original units is affected and must be re-measured, which restricts the efficiency evaluation in DEA. The main goal of this paper is to apply machine learning algorithms to overcome the shortcomings of the DEA models. On the other hand, in many real-world problems, there are some imprecise data due to incomplete or non-attainable information, errors in measurements, unquantifiable variables, or any other source of reason. In the DEA literature, many studies have focused on developing methods that incorporate uncertainty into the input/output values. The uncertain data can be reported as fuzzy data, stochastic data, and interval data. This paper considers the situation where each input/output value is selected from a symmetric box. First, we use a super-efficiency SBM model in the presence of uncertain data to construct the relative effective frontier and then apply the machine learning algorithms to construct a regression model and establish the absolute effective frontier. The proposed method has some advantages, compared to the existing methods. Also, the proposed model can better overcome the problems associated with DEA compared with the DEA in the presence of uncertain data and the neural network fusion outlined in the literature, so it can improve fusion efficiency. Manuscript profile

One of the interesting subjects that amuse the mind of researchers is surmising the correct classification of a new sample by using available data. Data Envelopment Analysis (DEA) and Discriminant Analysis (DA) can classify data by each one alone. DEA classifies as efficient and inefficient groups and DA classify by historical data. Merge these two methods is a powerful tool for classifying the data. Since, in the real world, in many cases we do not have the exact data, so we use imprecise data (e.g. fuzzy and interval data) in these cases. So, in this paper, we represent our new DEA-DA method by using Mixed-Integer Nonlinear Programming (MINLP) to classify with imprecise data to more than two groups. Then we represent an empirical example of our purpose method on the Iranian pharmaceutical stock companies' data. In our research, we divided pharmaceutical stock companies into four groups with imprecise data (fuzzy and interval data). Since, most of the classical DA models used for two groups, the advantage of the proposed model is beheld. The result shows that the model can predict and classify more than two groups (as many as we want) with imprecise data so correct. Manuscript profile

Due to the nonlinear and discrete nature of BCC (Banker, Charnes, and Cooper, [11]) models for determining the most efficient decision-making unit, it is practically impossible to evaluate the models' dual and, consequently, optimistic case. Thus, in this paper, the linear model with linear constraints proposed by Akhlaghi et al. [2] is used to investigate the dual equality of the model's robust problem and the optimistic case of the new model's dual under VRS uncertainty. The model proposed in this paper is novel in comparison to previous models because it solves the most efficient decision-making unit only once, without relying on uncertain data to determine its rank. The paper demonstrates how the proposed robust model can also ascertain the most efficient decision-making unit when uncertainty exists. Furthermore, the dual issues raised by robust counterparts in the new linear programming (LP) model are addressed to identify the most efficient decision-making unit. The robust counterpart is demonstrated to be equivalent to a linear program under interval uncertainty, and the dual of the robust counterpart is shown to be equal to the optimistic counterpart of the dual problem. Consequently, this study aims to demonstrate that the dual problem is equivalent to a decision-maker operating under optimal data, whereas the primal robust problem is equivalent to a decision-maker operating through the worst-case possible data scenario. Manuscript profile

On account of the existence of uncertainty, DEA occasionally faces the situation of imprecise data, especially when a set of DMUs include missing data, ordinal data, interval data, stochastic data, or fuzzy data. Therefore, how to evaluate the efficiency of a set of DMUs in interval environments is a problem worth studying. In this paper, we discussed the new method for evaluation and ranking interval data with stochastic bounds. The approach is exemplified by numerical examples. Manuscript profile

One of the new topics in DEA is the data with integer values. In DEA classic models, it is assumed that input and output variables have real values. However, in many cases, some inputs or outputs can have integer values. Measuring cost efficiency is another method to evaluate the performance and assess the capabilities of a single decision-making unit for manufacturing current products at a minimum cost with its input prices. In this paper, we proposed a model which is capable of calculating the cost efficiency in the absence of convexity when some of the input parameters have integer values, and then we implemented the mentioned model with a numerical example and discussed the results. Manuscript profile

Data envelopment analysis (DEA )has been extended to cross -efficiency evaluation for ranking decision making units (DEA) and eliminating unrealistic weighting schemes.Unfortunately,the nonunique optimal weights problem in DEA has reduced the usefulness of this extended method.Aiming at solving this problem,we first incorporate a target idenification model to get reachable targets for all the DMUs. Then, several secondary goal models are proposed for wights selection considering both desirable and undesirable cross-efficiency targets of all the DMUs. Compared with the traditional secondary goal models, the cross-efficiency targets are improved in that all targets are always reachable for the DMUs. In addition, the proposed models considered the DMUs, willingness to get close to their desirable cross-efficiency targets and to avoid their undesirable cross-efficiency targets simultaneously while the traditional secondary goal models considered only the ideal targets of the DMUs. Since usually some detailed data are available, and they have to figure range. In this paper we extend this model and secondary goals so that is able to calculate the cross efficiency of these conditions. Manuscript profile

Data envelopment analysis (DEA) creates many opportunities for collaboration between analyst and decision-maker. There are, however, situations in which all of the decision-making units (DMUs) fall under the umbrella of a centralized decision maker that oversees them. Many organizations such as bank branches, chain stores, … can do this. This centralized decision maker unit expect that resource allocation and revenue efficiency be in a way that DMUs not separately but in a group and simultaneously projected onto the efficiency frontier; as a result, it won’t be possible based on current DEA models. Therefore, centralized resource allocation or institutional allocation was formulated. There are situations in which centralized method presented in a central decision maker unit to allocate resources based on revenue efficiency. However, in reality value and rate are not often observed for all of the undesirable and desirable output units, which poses a problem in determining the revenue efficiency. Therefore, the best solution in these cases is to divide the outputs into two categories of known and unknown prices, which will be a more valid criterion for determining the revenue efficiency. In this paper, based on these methods, the revenue efficiency in branches of Pasargadae Bank will be analyzed and a comprehensive ranking will be made on these branches. Manuscript profile

زمینه و هدف: صنعت ساخت وساز و اجرای طرح های عمرانی، به عنوان یکی از عوامل آلودگی های زیست‌محیطی به شمار می رود. توجه به اثرات تخریبی و آلودگی های ایجاد شده از اجرای طرح های عمرانی، لزوم ارزیابی اثرات زیست‌محیطی و شناخت آن ها را در جهت کاهش اثرات، امری ضروری می سازد. هدف از انجام این پژوهش ارزیابی اثرات زیست‌محیطی پروژه آب رسانی روستایی در شهرستان بیرجند با استفاده از روش ماتریس لئوپولد ایرانی است.روش بررسی: در این پژوهش ارزیابی اثرات منفی زیست‌محیطی اجرای پروژه آب رسانی روستایی در شهرستان بیرجند طی سال 1398 در دو محیط فیزیکی-شیمیایی و بیولوژیکی و در فاز ساختمانی با استفاده از روش ماتریس ایرانی انجام پذیرفته است. به این منظور برای ارزیابی اثرات زیست‌محیطی حالت های اجرایی مختلفی برای اجرای فعالیت های پروژه تدوین شده و به ازای هر کدام از آن ها ماتریس لئوپولد تشکیل گردیده است.یافته ها: نتایج نشان داد که میانگین اثرات زیست‌محیطی در دوران ساخت در 7 حالت اجرایی موردبررسی بر محیط به میزان 58/1-، 95/1-، 15/2-، 5/2-، 2-، 21/2- و 22/2- است. همچنین تعداد پیامدهای زیست‌محیطی در آلودگی آب های سطحی و زیرزمینی بیشترین و مناطق حفاظت شده کمترین مقدار را دارند.بحث و نتیجه گیری: با توجه به تحلیل انجام شده در هیچ یک از ردیف ها و ستون های هفت ماتریس بررسی شده برای حالت های اجرایی، میانگین رده بندی کمتر از 1/3- یافت نشد، لذا انجام پروژه آب رسانی مورد تأیید می باشد. برای کاهش اثرات نیز کمترین میزان اثرات زیست‌محیطی هر فعالیت انتخاب گردید که باعث می شود میانگین اثرات زیست‌محیطی کل پروژه 52/1- گردد. Manuscript profile

هدف این پژوهش امکانسنجی کارآفرینی از طریق اکتساب به کمک شناسایی ، بررسی و تبیین عوامل موثر بر در بازار ایران می باشد. پژوهش حاضر از نظر هدف کاربردی و از نظر روش آمیخته(کیفی- کمی) است. در مرحله کیفی با استفاده از مطالعات کتابخانه ای و نظرسنجی از خبرگان پرسشنامه پژوهش تهیه و در مرحله کمی با استفاده از پرسشنامه تدوین شده داده هایی از جامعه مورد نظر جمع آوری گردید. جامعه آماری پژوهش حاضر، شامل کلیه کارآفرینان و سرمایه گذاران می باشد. برای نمونه گیری از روش غیر تصادفی هدفمند استفاده شد. به منظور تجزیه و تحلیل داده ها از آمار توصیفی و استنباطی( آزمون تی تک نمونه ای) استفاده شده است. با عنایت به نتایج این پژوهش، نتیجه میگیریم که استراتژی اکتساب می تواند به عنوان مسیری کوتاه و عملیاتی در شناسایی فرصتهای سرمایه گذاری و توسعه کسب و کار های کارآفرینانه مورد توجه قرار گیرید. Manuscript profile

سرمایه گذاری در پروژه‌ها از عوامل رشد اقتصادی و توسعه پایدار در کشورها است. از آنجا که سرمایه گذاری‌ها معمولا" در معرض ریسک‌های مختلفی هستند و به دلیل اهمیت بحث ریسک در انتخاب پروژه‌ها، همواره بر موضوع بهینه سازی انتخاب پروژه‌ها و تکنیک‌‌های بهینه سازی برای کاهش ریسک‌ها تأکید شده است. گروه‌های عمده ریسک در ارتباط با پروژه‌های سرمایه گذاری به دو گروه ریسک‌های بازار و سیاسی تقسیم بندی می‌شوند که نتیجه‌ی هر دو می‌تواند به کاهش بهره‌وری و افزایش هزینه‌های تمام شده در یک پروژه سرمایه‌گذاری بیانجامد. ریسک بازار از چهار ریسک اصلی به نام‌های ریسک تجاری، ریسک پیشبرد پروژه،ریسک بهره برداری و ریسک ساخت/ اتمام تشکیل شده ‌است. هدف این مقاله ارزیابی و رتبه بندی انواع ریسک‌های بازار در انتخاب انواع پروژه‌های سرمایه گذاری، بر اساس مدل‌های برنامه ریزی ریاضی با تکنیک ترکیبی DEA/AHPمی‌باشد بطوریکه با توجه به اوزان نسبی تعیین شده، میزان تأثیر کلی ریسک‌های بازار بر اولویت بندی پروژه‌ها مشخص می‌شود. پس از اجرای الگوریتم ارائه شده، ریسک پیشبرد پروژه اولویت اول، ریسک ساخت/ اتمام اولویت دوم، ریسک بهره برداری و ریسک تجاری در اولویت‌های سوم و چهارم قرار گرفتند تا سرمایه گذاران قادر به شناسایی و کاهش اثرات آن‌ها در پروژه‌ها باشند. Manuscript profile

Traditional data envelopment analysis (DEA) models evaluate the performance of decision-making units (DMUs) with the exact data and do not assume evaluation in the condition that the environment is uncertain. When some data are unknown, such as interval data, the DEA model is called imprecise DEA (IDEA). In this paper, we develop a new Malmquist productivity index(MPI) for dealing with interval data in DEA. Then, an approach for measuring the Malmquist productivity index using revenue efficiency is extended, too. The capabilities of the presented approach are shown by means of an example. Manuscript profile

In many applied programs in real-life problems, both physical inputs and out-puts are heterogeneous which in this case the efficient cost and income modelcan not apply to evaluate the cost and income of related turnover. So, a mea-surement based on the directional value of prot was presented which we havedeveloped it in this paper and have computed it for interval data. In fact, wehave measured the inefficiency of cost in the presence of interval data using thedirectional distance function which is most meaningful for those companiesthat their essential behavioral goals are maximizing the prot with least am-ambiguity. To this end, considering some branches of Tejarat bank in Iran, theefficiency of profit in presence of interval data is computed by means of thedistance directional function. Manuscript profile

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). 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-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets. Manuscript profile

The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method. Manuscript profile

Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0.0000001 respectively. This neural network can be used for prediction. Manuscript profile

Allocating fixed costs with undesirable data has recently been one of the most important issues for managers to discuss. Lack of attention to undesirable data may lead to incorrect cost allocation. Considering and determining undesirable inputs and outputs, data envelopment analysis (DEA) technique can be significantly helpful in determining the cost allocation strategy. In-puts and outputs are divided into two desirable and undesirable groups. Obviously, desirable inputs and undesirable outputs must be reduced and undesirable inputs and desirable outputs must be increased to improve performance. This manuscript presents two strategies for allocating fixed costs with undesirable data. In the first strategy, each decision making unit (DMU) first determines the minimum and maximum shares that it can receive from the fixed resources while the efficiency of that DMU and other DMUs re-mains the same after receiving the fixed resources. Finally, the decision maker chooses the fixed cost for each DMU between the minimum and maxi-mum cost values proposed. In the second strategy, the allocation of fixed costs is done using the CCR multiplicative model with undesirable data. The effectiveness of both methods is examined by an applied study on the commercial banks. Manuscript profile

Interval Data Envelopment Analysis (Interval DEA) is a methodology to assess the efficiency of decision-making units (DMUs) in the presence of interval data. Sensitivity analysis and sustainability evaluation of decision- making units are as the most important concerns of Decision Makers (DM). In the past decades, many scholars have been attracted to the sustainability evaluation of DMUs from different perspectives. This study focuses on the sensitivity analysis in DEA and proposes an approach to determine the sustainability radius of the cost efficiency of units with interval data. Potential application of our proposed methods is illustrated by a numerical example from the literature review. Manuscript profile

One of the most important factors of the development of an economy is the mergers or acquisitions (M&A) at the level of its active companies such as insurance companies. The main purpose of this study is to examine the efficiency of merger and acquisition before doing this process in the insurance industry using network data envelopment analysis and can select the companies that potentially facilitate achieving the purposes of the merger and acquisition process and improve of this action. For this purpose, in this study, first the efficiency of 20 insurance companies was measured through the Modified Slack-Based Measure (MSBM) in the two-stage data envelopment analysis approach during three years 2017, 2018 and 2019. Then, considering the calculated efficiency, Asia Insurance Company, Parsian, Dey, Pasargad, Kowsar and Taavon, which have had efficient performance in the last three years, were excluded from the calculations and other companies were selected for M&A . After ensuring that no monopoly is considered via Herfindahl- Hirschman Index, M&A is performed and then the overall efficiency was measured and it was divided into three parts: technical, harmony and scale. The results showed that the two consolidations Dana-Mihan and Dana-Sina had the best efficiency and the three consolidations Alborz-Mellat, Sina-Arman and Sina-Sarmad had the lowest efficiency and potential for the highest improvement. Calculations also showed that if the scale effect in the composition is greater than 1, then the coordination effect is smaller than 1 and the inverse relationship are not necessarily satisfied. Manuscript profile

One important issue in data envelopment analysis (DEA) which has been studied by many researchers is returns to scale (RTS). The authors developed DEA models to evaluate the efficiency of two-stage networks in returns to scale variable and introduced a new definition to determine return to scale classification in two-stage networks. The current article proposed an approach for determining the stability region of returns to scale classification in two-stage network DEA. The data were collected from insurance companies in Iran in 2019. We consider the insurance industry process as a two-stage network; the stage of marketing and that of investment. The effectiveness of insurance companies was evaluated, and, after determining the classification of returns to scale, we found a sustainability interval to classify returns to their scale. Manuscript profile

In traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and …. may not prove to be economical. The key objective of the proposed algorithm is to segregate the efficient units from that of the other units. In order to gain access to this objective, effectual indexes were created; and taken to assist, in regards the DEA concepts and the type of business (under study), to survey the indexes, which were relatively operative. Subsequently, with the help of one of the clustering techniques and the ‘concept of dominance’, the efficient units were absolved from the inefficient ones and a DEA model was developed from an aggregate of the efficient units. By eliminating the inefficient units, the number of units which played a role in the construction of a DEA model, diminished. As a result, the speed of the computational process of the scores related to the efficient units increased. The algorithm designed to measure the various branches of one of the mercantile banks of Iran with financial indexes was implemented; resulting in the fact that, the algorithm has the capacity of gaining expansion towards big data. Manuscript profile

Data Envelopment Analysis (DEA) is an impeccable approach based on mathematical programming for the efficiency measurement of homogeneous Decision-Making Units (DMUs). One of the topics of interest in data envelopment analysis (DEA) is the sensitivity and stability analysis of a specific DMU that determines ranges within which all data may be altered for any DMU before a reclassification from efficient to inefficient status (or vice versa) happens. In many real-world applications, the managers to estimate the under supervision DMUs encounter stochastic data and require a way to deal with the sensitivity analysis of DMUs with this special data. In DEA, efficient DMUs are of primary importance as they define the efficient frontier. The intent of this paper is to present the sensitivity analysis with stochastic data for efficient DMUs when inputs and outputs are stochastic and variations in the data are simultaneously considered for all DMUs. The models explained in this paper for treating sensitivity analysis in DEA are expanded by according them chance-constrained programming formulations. The ordinary route used in chance-constrained programming is followed here by replacing these stochastic models with their deterministic equivalents. The optimal solution of these models leads to allowable input/ output variations. Manuscript profile

AbstractAs the calculated efficiencies for the DMUs is relative, so each DMU attempts to improve its performance to don’t miss the position of efficiency in compete with other DMUs. Generally, the performance of DMUs can be evaluated from two perspectives-optimistic and pessimistic. A part of data envelopment analysis examines the sensitivity of the set of efficient DMUs to changes in input and output values. In real world, DEA is sometimes faced with fuzzy and interval inputs and/or outputs. In this paper we focus on the one of important subjects of sensitivity analysis and present an algorithm which uses the classic fuzzy DEA models that can determine the relative efficiency security margin of DMUs with fuzzy and interval inputs and outputs and their simultaneous perturbation. Of course, in addition to the optimistic frontier, we also consider the pessimistic frontier for the observed DMUs and call it the inefficiency improvement margin. With this information, the managers of companies can identify their closest threat and improve their performance in order to keep their position in ranking. Some numerical examples for illustration are given. Manuscript profile

Over the last few decades, there has been a dramatic increase in public attention to environmental issues. As a consequence of the growing concerns about environmental quality, climate change, and pollutant emission, which are key elements of sustainable development, one of the main challenges is measuring environmental efficiency. The main purpose of this study is to evaluate the ecological efficiency of countries and rank countries based on data envelopment analysis (DEA) method, considering the favorable and unfavorable outputs (7 inputs and 7 outputs) affecting climate change in 2020 in 176 countries. The units were evaluated by CCR model and Also AP model in order to ranking both efficient units and inefficient units. The results show that the environmental efficiency of the selected countries is 80.60% on average, of which Iran ranks 140th with an efficiency of 0.58 and Iceland, Singapore and Lesotho have the highest environmental efficiency, respectively, as well as Sierra Leon, the Philippines, and Pakistan have the lowest environmental performance, respectively. Manuscript profile

In this article, we investigate a new continuous linear model with constraints for the direct selection of the most efficient unit in the analysis of data coverage presented by Akhlaghi et al. (2021) on uncertainty robust optimization. Considering the importance of incorporating uncertainty into performance evaluation models in the real world and its increasing application in various problems, we propose a robust optimization approach. Given the discrete and non-convex nature of the introduced models for selecting the most efficient decision-making unit, examining the dual and finding an optimistic scenario is practically impossible. Therefore, by utilizing the linear model presented by Akhlaghi et al. (2021) with constraints for identifying the most efficient unit, we can investigate the robustness of the desired model using(BS )Bertsimas and Sim's (2004) robust estimation method while also considering uncertainty. We aim to demonstrate that employing a robust formulation leads to reliable performance in uncertain conditions Manuscript profile

بانک ها به عنوان اصلی ترین بخش سیستم مالی نقش مهی در توسعه اقتصادی هر کشور دارند. محاسبه کارایی و در نتیجه یافتن نقاط قوت و ضعف شعب، تاثیر بسزایی در افزایش بهره وری بانک ها دارد. تحلیل پوششی داده ها یکی از تکنیک های ارزیابی عملکرد می باشد که علاوه بر محاسبه کارایی نسبی قادر به معرفی نقاط الگو برای واحدهای تصمیم گیرنده ناکارا است. این تکنیک قادر به ارزیابی کارایی واحدهای تصمیم گیرنده با چندین ورودی وچندین خروجی می باشد. در این مقاله با استفاده از تکنیک تحلیل پوششی داده ها، مدلی برای ارزیابی، تحلیل حساسیت و الگویابی 18 شعبه یکی از بانک های تجاری ایران با نسبت های مالی ارایه شده است. بدین منظور مدلی برای تخمین خروجی با داده های نسبتی طراحی شده است که با تغییر مقادیر ورودی، میزان تغییرات مورد نیاز در خروجی ها برای حفظ کارایی و همچنین حفظ رتبه با استفاده از مدل داده نسبتی پیشنهادی قابل محاسبه می باشد. Manuscript profile

در گذشته تلاش برای مفهوم سازی کلیه خریدها بر اساس مدل LBO و از دریچه تئوری نمایندگی بوده است که این نگاه به گذشته، توانایی مهندسین مالی را برای ایجاد مدل و ابزار خرید با انگیزه کارآفرینی، به شدت محدود کرده است. مدل های کارآفرینی از طریق اکتساب(ETA)، دریچه ای جهت احیای مجدد کسب وکارها از طریق خرید مشاغل کوچک با ظرفیت کارآفرینی به جای شروع یک کسب و کار نوپا است. در این پژوهش با توجه به شاخص های اثرگذار کارایی مدل های کارآفرینی از طریق اکتساب را با استفاده از روش تحلیل پوششی داده ها بر اساس مدل های BCC و CCR از دیدگاه کارآفرینان و سرمایه گذاران در بازار ایران مورد بررسی قرار داده ایم و در نهایت براساس مدل اندرسون و پیترسون(A&P) رتبه بندی جامعی از مدل ها ارائه شده است.نتایج نشان دهنده این است که بر اساس مدل BCC و CCR، مدل تامین مالی جمعی (C) از دیدگاه کارآفرینان و مدل انکوباتورها (I) از دیدگاه سرمایه گذاران ناکارا بوده است. بر اساس مدل اندرسون و پیترسون مدل بهینه از دیدگاه سرمایه گذاران مدل اسپانسر(S) و از دیدگاه کارآفرینان، مدل خوداتکایی (SL) با بیشترین میزان کارایی و انطباق با توجه به شاخص های در نظر گرفته شده بود. Manuscript profile

هدف از پژوهش پیش رو، ارائه مدل تحلیل پوششی داده های پنجره ای فازی با هدف ارزیابی عملکرد مالی سهام در خلال دور ه های زمانی مختلف تحت عدم قطعیت داده ها می باشد. به عبارت دیگر در این تحقیق تلاش می شود تا یک رویکرد نوین ارزیابی عملکرد سهام با قابلیت پیاده سازی در حضور داده های پانل غیر قطعی ارائه گردد. زیرا بهره گیری از اطلاعات مربوط به چند دوره زمانی مختلف به جای یک دوره زمانی و هم چنین در نظر گرفتن عدم قطعیت موجود در داده ها، می توانند منجر به نتایج قابل اتکاتری در فرایند ارزیابی عملکرد سهام گردند. لازم به توضیح است که در مدل سازی و ارائه رویکرد مذکور، از روش های تحلیل پوششی داده ها، تحلیل پنجره ای و برنامه ریزی امکانی بهره گرفته شده است. در نهایت نیز به منظور آشنایی با چگونگی پیاده سازی رویکرد پیشنهادی پژوهش، مدل تحلیل پوششی داده های پنجره ای فازی بر روی پنج سهم از صنعت فرآورده های شیمیایی در بورس اوراق بهادار تهران برای چهار دوره زمانی از سال 1392 الی سال 1395 اجرا و نتایج مورد تجزیه و تحلیل قرار گرفته اند که حاکی از کارآمدی رویکرد مذکور می باشند. Manuscript profile