فهرس المقالات Interval data

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  1 - مرزهای دوگانه در تحلیل پوششی داده های نامحدب با داده های بازه ای:ارزیابی کارایی و ناکارایی و تحلیل پایداری
  نسیم نصرآبادی شیدا آیتی
  10.30495/jnrm.2021.15726
  مدلهای اساسی تحلیل پوششی داده ها به طور ذاتی ارزیابی واحدهای تصمیم گیرنده را با دیدگاه خوشبینانه انجام میدهند، به این مفهوم که ارزیابی عملکرد هر واحد تصمیم گیرنده از طریق مقایسه آن واحد با مرز کارایی انجام میشود. مرز کارایی در واقع مرز متشکل از همه واحدهایی است که نشان أکثر

  مدلهای اساسی تحلیل پوششی داده ها به طور ذاتی ارزیابی واحدهای تصمیم گیرنده را با دیدگاه خوشبینانه انجام میدهند، به این مفهوم که ارزیابی عملکرد هر واحد تصمیم گیرنده از طریق مقایسه آن واحد با مرز کارایی انجام میشود. مرز کارایی در واقع مرز متشکل از همه واحدهایی است که نشان دهنده بهترین عملکرد هستند. اگر یک فعالیت روی مرز کارایی قرار داشته باشد کاملا کارا و در غیر این صورت غیرکارا نامیده میشود. به منظور ارائه یک ارزیابی دقیقتر می توان وضعیت واحدهای تصمیم گیرنده را با دیدگاه بدبینانه نیز مورد بررسی قرار داد، به این مفهوم که مرزی تحت عنوان مرز ناکارایی متشکل از همه واحدهایی که بدترین عملکرد را دارند، تشکیل داده و سپس عملکرد هر واحد تصمیم گیرنده را نسبت به آن ارزیابی نمود، به این صورت که هر چه یک واحد تصمیم گیرنده به مرز ناکارایی نزدیکتر باشد، ناکاراتر تلقی میشود. به صورت مشابه یک فعالیت را کاملا ناکارا گوییم اگر روی مرز ناکارایی قرار داشته باشد. در غیر این صورت آن را غیر ناکارا می نامیم. در این مقاله با در نظر گرفتن این فرض که مجموعه امکان تولید نامحدب است، به تحلیل کارایی و ناکارایی واحدهای تحت بررسی پرداخته و آنها را در دو رده کاملا (نا)کارا و غیر (نا)کارا افراز میکنیم. سپس مفهوم پایداری افراز را در تحلیل کارایی و ناکارایی مورد بررسی قرار میدهیم. در نهایت با فرض این که واحدهای تحت ارزیابی دارای ورودی و خروجی بازه ای هستد، به ارزیابی کارایی و ناکارایی آنها می‌پردازیم. تفاصيل المقالة
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  2 - رویکرد معادل‌سازی دوره‌ای با داده‌های بازه‌ای در ارزیابی کارایی پویا شبکه‌های چند مرحله-ای
  علیرضا ابراهیمی بقا فرهاد حسین زاده لطفی علیرضا امیرتیموری محسن رستمی مال خلیفه
  در این مقاله ارزیابی کارایی دینامیکی واحدهایی با ساختارشبکه‌ای چندمرحله‌ای تحت داده‌های بازه‌ای برای مدل‌های تحلیل پوششی شبکه‌ای(NDEA) انجام می‌شود. هدف این مطالعه مدل‌سازی با ایده معاد‌ل‌سازی دوره‌ای به منظور ارزیابی میزان کارایی پویا زیر‌ساختارها و شبکه‌ نهایی، با داد أکثر

  در این مقاله ارزیابی کارایی دینامیکی واحدهایی با ساختارشبکه‌ای چندمرحله‌ای تحت داده‌های بازه‌ای برای مدل‌های تحلیل پوششی شبکه‌ای(NDEA) انجام می‌شود. هدف این مطالعه مدل‌سازی با ایده معاد‌ل‌سازی دوره‌ای به منظور ارزیابی میزان کارایی پویا زیر‌ساختارها و شبکه‌ نهایی، با داده‌های بازه‌ای در دوره‌های زمانی معین می‌باشد. مدل پیشنهادی این پژوهش، یک مدل غیرشعاعی با تکنولوژی تعریف شده و بر اساس الگوی واحد کارای قوی در قالب یک شبکه تبدیلی‌ مجازی است که قابلیت ارزیابی چندین زیر فرایند را دارد. همچنین این مدل تعامل و ارتباط بین زیر فرایندها و کل شبکه را در هر مرحله و هر دوره به صورت تام در نظر می‌گیرد و بر اساس داده‌های بازه‌ای، کارایی شبکه نهایی (شبکه کل) و زیرفرایندها را دریک بازه، محاسبه می-نماید. این مدل مناسبات و تنظیمات بین زیر فرایندها را در یک ساختار شبکه‌ای چند مرحله‌ای پویا، برای کارا شدن فراهم می-نماید. همچنین با یک مثال کاربردی بر مبنای ایده این پژوهش و مدل‌های استنتاجی آن، کارایی دینامیکی در یک دوره چهار ساله در واحدهای تولیدی با داده‌های بازه‌ای وکراندار مورد ارزیابی و سنجش قرار گرفته است. تفاصيل المقالة
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  3 - محاسبه‌ی بازه‌ی کارایی واحدهای تصمیم‌گیرنده دارای ورودی و خروجی‌های بازه‌ای با حضور داده‌های منفی
  محسن رستمی مال خلیفه فاطمه سادات سیداسماعیلی
  فرض اساسی در الگوهای تحلیل پوششی داده‌ها (مثل مدل‌های CCR و BCC)، این است که مقدار داده­های مربوط به ورودی­ها و خروجی­ها عددی دقیق و مثبتی می­باشد، ولی بسیاری از اوقات در شرایط واقعی کسب و کار، تعیین مقدار عددی دقیق برای برخی ورودی‌ها و یا خروجی‌ها امکان أکثر

  فرض اساسی در الگوهای تحلیل پوششی داده‌ها (مثل مدل‌های CCR و BCC)، این است که مقدار داده­های مربوط به ورودی­ها و خروجی­ها عددی دقیق و مثبتی می­باشد، ولی بسیاری از اوقات در شرایط واقعی کسب و کار، تعیین مقدار عددی دقیق برای برخی ورودی‌ها و یا خروجی‌ها امکان پذیر نیست. به همین منظور در سال­های اخیر در تحلیل پوششی داده‌ها مدل­های متفاوتی برای داده­های غیر دقیق مطرح شد و همچنین پژوهش­های زیادی در زمینه DEA انجام شده است که قادر به ارزیابی کارایی با داده­های منفی می­باشد، الگوی تحلیل پوششی داده-های بازه­ای منفی که در این تحقیق معرفی و مورد استفاده قرار گرفته است عدم قطعیت را هم در ورودی­ها و هم در خروجی­ها مورد توجه قرار می­دهد و نتایج پایدارتر و قابل اطمینان­تری را برای تصمیم­گیری در اختیار کاربر قرار می­دهد. حال در این مقاله مدلی ارائه می­دهیم که قادر است بازه کارایی واحدها با ورودی و خروجی بازه­ای که بعضی از شاخص­ها می­توانند منفی هم باشند را محاسبه می­کند و در ادامه ثابت می­کنیم بازه کارایی که این مدل به ما می­دهد نسبت به بازه کارایی مدل­های قبلی ارائه شده، دقیق­تر است و در نهایت نیز ده واحد تصمیم­گیری با داده­های غیر دقیق (بازه­ای) منفی با مدل پیشنهادی مورد بررسی قرار می­گیرند و نتایج مدل پیشنهادی با نتایج مدل­های قبلی مورد مقایسه قرار می­گیرد. تفاصيل المقالة
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  4 - ارزیابی عملکرد بنگاه های سرمایه گذاری تحت عدم قطعیت
  پژمان پیکانی فرهاد حسین زاده لطفی عمران محمدی رضا تهرانی
  صندوق ها و شرکت های سرمایه گذاری یکی از مهم ترین نهاد ها و ساز و کار های مفید سرمایه گذاری در بازار های سرمایه هستند. از این رو ارزیابی عملکرد آنها با هدف شناسایی بنگاه های سرمایه گذاری کارآمد و هم چنین ارائه راه کار اصلاحی برای بنگاه های نا کارآمد، امری ضروری است. هدف أکثر

  صندوق ها و شرکت های سرمایه گذاری یکی از مهم ترین نهاد ها و ساز و کار های مفید سرمایه گذاری در بازار های سرمایه هستند. از این رو ارزیابی عملکرد آنها با هدف شناسایی بنگاه های سرمایه گذاری کارآمد و هم چنین ارائه راه کار اصلاحی برای بنگاه های نا کارآمد، امری ضروری است. هدف از پژوهش پیش رو ارائه یک رویکرد نوین به منظور ارزیابی عملکرد و طبقه بندی شرکت های سرمایه گذاری در شرایط عدم قطعیت می باشد. با توجه به ساختار دو مرحله ای حاکم بر بنگاه های سرمایه گذاری، در این پژوهش از رویکرد تحلیل پوششی داده های شبکه ای به منظور ارزیابی عملکرد هر یک از مراحل و کل شرکت سرمایه گذاری، استفاده می گردد. هم چنین به منظور در نظر گرفتن عدم قطعیت موجود در داده ها، مدل تحلیل پوششی داده های شبکه ای بازه ای با ساختار دو مرحله ای ارائه می گردد. با بهره گیری از داده های مربوط به 10 شرکت سرمایه گذاری فعال در بورس اوراق بهادار تهران، رویکرد پیشنهادی پژوهش پیاده سازی شده است و تمامی شرکت ها مورد ارزیابی و طبقه بندی قرار گرفته اند. نتایج مربوط به مدل تحلیل پوششی داده های شبکه ای بازه ای مبتنی بر رویکرد غیر مشارکتی، حاکی از توانمندی و کارآمدی رویکرد پیشنهادی پژوهش در ارزیابی عملکرد و طبقه بندی بنگاه های سرمایه گذاری تحت عدم قطعیت داده ها است. تفاصيل المقالة
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  5 - A One-Model Method Based on a Relaxed Combination of Inputs for Congestion Assessment in Interval Data Envelopment Analysis
  Masoumeh Hajiani Reza Fallahnejad
  It is of special importance to calculate and recognize the quantity of congestion, as one of the major sources of inefficiency in different areas, and attempt to resolve it in order for reducing the costs and increasing the output. To date, various methods have been pro أکثر

  It is of special importance to calculate and recognize the quantity of congestion, as one of the major sources of inefficiency in different areas, and attempt to resolve it in order for reducing the costs and increasing the output. To date, various methods have been proposed for the calculation of congestion in classic data envelopment analysis (DEA) with precise input and output values while, in the real world, the input and output values are imprecise in most of the cases. The present paper proposes a new model for calculating the congestion interval for interval data in such cases that the interval inputs are not constrained to the selection of dominant projection points and, thereby, more outputs can be generated for the projection points. The proposed method is used for assessing the inefficiency and finding the values of congestion in the inputs of 20 bank branches. تفاصيل المقالة
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  6 - Estimating Most Productive Scale Size of the provinces of Iran in the Employment sector using Interval data in Imprecise Data Envelopment Analysis(IDEA)
  Mohammad Khodabakhshi saeed papi reza fallahnejad Masoume Yazdanpanah Maryaki
  Unemployment is one of the most important economic problems in Iran, so that many of its managers plan to increase employment rates. Increasing the employment rate needs to increase economic productivity which DEA is one of the most appropriate evaluation methods for es أکثر

  Unemployment is one of the most important economic problems in Iran, so that many of its managers plan to increase employment rates. Increasing the employment rate needs to increase economic productivity which DEA is one of the most appropriate evaluation methods for estimating the productivity of similar organizations. Employment in the amount of data input and output can be just interval. In this study by solving two models, using one of which the upper bound for efficiency and using the other, the lower bound for decision making units efficiency is acquired, we provide a new model for Most productive scale size with interval data. The main purpose of this study is to determine the productivity of Iran and sensitive indicators to provide a fundamental solution to exit from unemployment. The economic sector managers can do more exact planning for economic growth. تفاصيل المقالة
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  7 - A combined machine learning algorithms and Interval DEA method for measuring predicting the efficiency
  Hasan Babaei Keshteli Mohsen Rostamy-Malkhalifeh
  10.30495/ijdea.2022.69371.1183
  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, data أکثر

  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%. تفاصيل المقالة
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  8 - DEA-TOPSIS with interval data
  Zohreh Iravani Mahnaz Ahadzadeh Namin
  10.30495/ijdea.2023.74349.1200
  Since it plays a significant role in the best choice of industry and science, it is always chosen to find the job that can be more accurate. One of these methods is the TOPSIS method.In this article, after introducing the DEA and TOPSIS methods, we will introduce the TO أکثر

  Since it plays a significant role in the best choice of industry and science, it is always chosen to find the job that can be more accurate. One of these methods is the TOPSIS method.In this article, after introducing the DEA and TOPSIS methods, we will introduce the TOPSIS-DEA method and then we will expand this method for interval criteria. In this method, there is no need to know the exact weight of the qualities, and this method deals with the best option without changing the qualitative qualities. This method is a suitable method for choosing the best option when the data is an interval. And it is faster than the classic method. تفاصيل المقالة
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  9 - Robust optimization for identifying the most efficient decision making unit in data envelopment analysis
  Reza Akhlaghi Mohsen Rostamy-Malkhalifeh Alireza Amirteimoori Sohrab Kordrostami
  10.30495/ijdea.2023.75220.1204
  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 lin أکثر

  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. تفاصيل المقالة
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  10 - The Calculation of Unit's Efficiency by Using the Interval Balance Index and the Interval TOPSIS
  B. Babazadehah E. Najafi M. AhadzadehNamin Y. jafari E. Ebrahimi
  Data envelopment analysis (DEA) is a technique for measuring the efficiency of decision making units. In all models of the DEA, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. Given the possible large number o أکثر

  Data envelopment analysis (DEA) is a technique for measuring the efficiency of decision making units. In all models of the DEA, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. Given the possible large number of functional units, we use various ranking methods for evaluating units. One of the rating methods is Balance index and Topsis. This method has been used for categorical data. In this paper, we assume data as interval, introduce the interval Balance index and the interval Topsis and run it on a single example. تفاصيل المقالة
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  11 - The Calculation of Unit's Efficiency by Using the Interval
  B. Babazadeh E. Najafi M. AhadzadehNamin Y. jafari Z. Ebrahimi
  Data Envelopment Analysis )DEA( is a technique for measuring the efficiency of decision makingunits. In all models of the DEA, for each unit under assessment, the numerical efficiency which maybe less than or equal to one is obtained. Given the possible large number of أکثر

  Data Envelopment Analysis )DEA( is a technique for measuring the efficiency of decision makingunits. In all models of the DEA, for each unit under assessment, the numerical efficiency which maybe less than or equal to one is obtained. Given the possible large number of efficiency units forevaluating units, we use various methods of ranking. span style="font-family: Cambria Math;font-size:8pt;color:rgb(0,0,0);font-style:normal;font-variant:normal L1norm is one of the methods of ranking. Thismethod has been used for categorical data. In this paper, we assume data as interval and introducespan style="font-family: Cambria Math;font-size:8pt;color:rgb(0,0,0);font-style:normal;font-variant:normal L1norm andrun it on a single example. تفاصيل المقالة
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  12 - The Efficiency of MSBM Model with Imprecise Data (Interval)
  F. seyed Esmaeili
  Data Envelopment Analysis (DEA) is a mathematical programming-based approach for evaluates the relative efficiency of a set of DMUs (Decision Making Units). The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other أکثر

  Data Envelopment Analysis (DEA) is a mathematical programming-based approach for evaluates the relative efficiency of a set of DMUs (Decision Making Units). The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other DMUs in the PPS (Production Possibility Set). Also, in Data Envelopment Analysis various models have been developed in order to evaluate the performance of decision-making units with negative data. The Modified Slack Based Measure (MSBM) model is from collective models family. This modified model is based on slack-based measure (SBM). Also the early models of data envelope analysis considered inputs and outputs as precise data. However, in studies about the data envelope analysis, some methods presented for applying imprecise data. Based on this, data envelope analysis models with interval data have been developed. In this paper, the MSBM model is investigated in presence of interval negative data, and then the efficiency of the model with imprecise data (interval) is evaluated. The efficiency of ten decision-making units is evaluated. تفاصيل المقالة
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  13 - Efficiency Evaluation and Ranking DMUs in the Presence of Interval Data with Stochastic Bounds
  Hamid Sharafi Mohsen Rostamy-Malkhalifeh Alireza Salehi Mohammad Izadikhah
  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 DMU أکثر

  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. تفاصيل المقالة
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  14 - Efficiency of decision making units in network DEA using interval data
  T Hassani M Rostamy-Malkhalifeh
  In this paper, the difference between multiplicative and envelopment models of network DEA is examined, in which network DEA multiplicative model is able to calculate efficiency and the envelopment model can calculate the projection on the frontier. Here, a model is pre أکثر

  In this paper, the difference between multiplicative and envelopment models of network DEA is examined, in which network DEA multiplicative model is able to calculate efficiency and the envelopment model can calculate the projection on the frontier. Here, a model is presented that can calculate both frontier projection and efficiency in network DEA. Since in real world, many data are interval data, we present a model in this article that calculates the efficiency of the units being evaluated by such these interval data. Since data are as intervals, the resulting efficiencies are calculated as intervals. We present two models for calculating the lower and upper bounds for any DMU and prove that these models give upper and lower bounds of efficiency. تفاصيل المقالة
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  15 - Robust Optimization and Confidence Interval DEA for Efficiency Evaluation with Intervals Case Study: Evaluating CRM Units in a Call Center in Tehran
  Shabnam Mohammadi Mohammad Jafar Tarokh Emran Mohammdi
  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 in أکثر

  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. تفاصيل المقالة
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  16 - Interval Malmquist Productivity Index in DEA
  N. Aghayi B.H. Maleki
  Data envelopment analysis is a method for evaluating the relative efficiency of a collection of decision making units. The DEA classic models calculate each unit’s efficiency in the best condition, meaning that finds a weight that the DMU is at its maximum efficie أکثر

  Data envelopment analysis is a method for evaluating the relative efficiency of a collection of decision making units. The DEA classic models calculate each unit’s efficiency in the best condition, meaning that finds a weight that the DMU is at its maximum efficiency. In this paper, utilizing the directional distance function model in the presence of undesirable outputs, the efficiency of each unit has been calculated in the best and worst condition and an efficiency interval for each DMU is designated and then with aid from these efficiency interval, we present an interval for each unit with a proportionate Malmquist productivity index, that these intervals indicate the progression or regression of each DMU. تفاصيل المقالة
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  17 - An Interval Model in Interdiction Network Flow
  R. Keshavarzi H. Salehi ‎Fathabadi‎
  One of the key issues raised in networks flow is its interdiction. Keshavarzi ‎et al. ‎scrutinized the issue with the multi-sources and multi-sinks conditions in mind while considering the specific conditions of flow being sent from sources to sinks ‎[R. Kes أکثر

  One of the key issues raised in networks flow is its interdiction. Keshavarzi ‎et al. ‎scrutinized the issue with the multi-sources and multi-sinks conditions in mind while considering the specific conditions of flow being sent from sources to sinks ‎[R. Keshavarzi ‎et al.‎, Multi-source-sinks network flow interdiction problem, ‎International Journal of Academic Research,‎ 2015]. Moreover, the matter was also examined in the state of multi-interdictors and a practical solution was presented ‎[‎K‎eshavarzi and, Salehi, Multi commodity multi source-sinks network flow interdiction problem with several interdictors, ‎Journal of Engineering and Applied Sciences,‎ 2015]‎. In this paper, the networks flow interdiction in multi-source and multi-sink conditions was addressed; meanwhile, bearing in mind the uncertain data (from a specified beginning to an ending interval), an optimal interval was presented. Finally, a numerical example for this issue was provided and then solved by the program "Lingo"‎.‎ تفاصيل المقالة
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  18 - Data envelopment analysis with imprecise data revisited
  Mohammad Izadikhah Dimitris K. Despotis
  Wang et al. (2005) proposed a pair of data envelopment analysis (DEA) models to deal with the efficiency assessment of decision-making units (DMU) in the presence of interval input/output data. Their approach was developed with reference to an earlier approach proposed أکثر

  Wang et al. (2005) proposed a pair of data envelopment analysis (DEA) models to deal with the efficiency assessment of decision-making units (DMU) in the presence of interval input/output data. Their approach was developed with reference to an earlier approach proposed by Despotis and Smirlis (2002) for the same problem. Given that the input/output data are given as interval numbers, the efficiency scores are interval measures as well. In such a setting, both approaches provide lower and upper bounds for the efficiency scores. Wang et al. (2005) claim that the lower and upper bounds calculated in Despotis and Smirlis (2002) are incorrect. Then, they present different models to calculate the true bounds. In this paper, we counter-argue their claim and we show that the Despotis and Smirlis bounds are correct and those provided in Wang et al. are estimated in a manner that they fail to satisfy an obvious property that they should possess. We illustrate our arguments with a counterexample that was originally used in Wang et. al (2005). تفاصيل المقالة
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  19 - Imprecise Revenue Efficiency under Productivity Change
  Mohsen Rostamy-Malkhalifeh
  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 mo أکثر

  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. تفاصيل المقالة
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  20 - Measuring the Interval industry cost efficiency score in DEA
  Ghasem Tohidi Simin Tohidnia
  10.22034/amfa.2020.1880449.1308
  In this paper we extend the concept of "cost minimizing industry structure" and develop two DEA models for dealing with imprecise data. The main aim of this study is to propose an approach to compute the industry cost efficiency measure in the presence of interval data. أکثر

  In this paper we extend the concept of "cost minimizing industry structure" and develop two DEA models for dealing with imprecise data. The main aim of this study is to propose an approach to compute the industry cost efficiency measure in the presence of interval data. We will see that the value obtained by the proposed approach is an interval value. The lower bound and upper bound of the interval industry cost efficiency measure are computed and then decomposed into three components to examine the relationship between them and the lower and upper bounds of the individual interval cost efficiency measures. We also define the cost efficient organization of the industry as a set of DMUs, which minimizes the total cost of producing the interval industry output vector. In fact, this paper determines the optimal number of DMUs and the reallocation of the industry observed outputs among them. We hereby determine the effects of the optimal number of DMUs and the reallocation of outputs among them on the interval industry cost efficiency measure. Finally, a numerical example will be presented to illustrate the proposed approach. تفاصيل المقالة
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  21 - The sustainability radius of the cost efficiency in Interval Data Envelopment Analysis: A case study from Tehran Stocks
  Esmaeil Mombini Mohsen Rostamy-Malkhalifeh Mansour Saraj
  10.22034/amfa.2021.1917327.1528
  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 أکثر

  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. تفاصيل المقالة
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  22 - مدل‌های تحلیل پوششی داده‌های بازه‌ای مبتنی بر TOPSIS
  Hossein Azizi Alireza Amirteimoori Sohrab Kordrostami
  تحلیل پوششی داده‌ها (DEA) روشی برای سنجش عملکرد گروهی از واحدهای تصمیم‌گیری (DMUها) است که از ورودی‌های متعدد برای تولید خروجی‌های متعدد استفاده می‌کنند. این مقاله دو DMUی مجازی به نام DMUی ایده‌آل و DMUی آنتی‌ایده‌آل را وارد DEAی بازه‌ای می‌کند. مدل‌های DEAی بازه‌ای به أکثر

  تحلیل پوششی داده‌ها (DEA) روشی برای سنجش عملکرد گروهی از واحدهای تصمیم‌گیری (DMUها) است که از ورودی‌های متعدد برای تولید خروجی‌های متعدد استفاده می‌کنند. این مقاله دو DMUی مجازی به نام DMUی ایده‌آل و DMUی آنتی‌ایده‌آل را وارد DEAی بازه‌ای می‌کند. مدل‌های DEAی بازه‌ای به دست آمده به ترتیب DEAی بازه‌ای با DMUهای ایده‌آل و آنتی‌ایده‌آل نامیده می‌شوند. یکی از آنها DMUها را از دیدگاه کارآیی خوشبینانه ارزیابی می‌کند، در حالی که دیگری آنها را از دیدگاه کارآیی بدبینانه ارزیابی می‌کند. این دو کارآیی بازه‌ای متمایز با هم ترکیب می‌شوند و یک شاخص جامع به نام نزدیکی نسبی به DMUی ایده‌آل را درست مانند رویکرد روش ترجیح ترتیب بر اساس شباهت به جواب ایده‌آل در تصمیم چندشاخصی تشکیل می‌دهند. سپس از شاخص نزدیکی نسبی به عنوان سنجش کلی هر DMU استفاده می‌شود و بر مبنای آن یک رتبه‌بندی کلی برای همه‌ی DMUها به دست می‌آید. یک مثال نیز در زمینه‌ی ارزیابی عملکرد بیست شعبه‌ی بانک ارائه خواهد شد که نشان می‌دهد که رویکرد DEAی بازه‌ای پیشنهادی یک روش ساده، مؤثر و عملی برای اندازه‌گیری عملکرد در موقعیت‌های زندگی واقعی است. تفاصيل المقالة
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  23 - Interval Efficiency Assessment in Network Structure Based on Cross –Efficiency
  nasim roudabr seyed esmaeil najafi
  As we know, in evaluating of DMUs some of them might be efficient, so ranking of them have a high significant. One of the ranking methods is cross-efficiency. Cross efficiency evaluation in data envelopment analysis (DEA) is a commonly used skill for ranking decision ma أکثر

  As we know, in evaluating of DMUs some of them might be efficient, so ranking of them have a high significant. One of the ranking methods is cross-efficiency. Cross efficiency evaluation in data envelopment analysis (DEA) is a commonly used skill for ranking decision making units (DMUs). Since, many studies ignore the intra-organizational communication and consider DMUs as a black box. For significant of this subject, we applied cross-efficiency for network DMUs. However, In view of the fact that precise input and output data may not always be available in real world due to the existence of uncertainty, we have developed the model with interval data. the existing classical interval DEA method is not able to rank the DMUs, but can only classify them as efficient or inefficient , so this paper improve that. The proposed method can be used for each network that includes DMUs with two stages in production process. However, this paper is the first study that examined cross efficiency of DMUs in structure framework with interval data. the new approach enables us to ranking of first stage for n DMU and second stages of them. DMUs with the best rank can be used as benchmark for improving efficiency of other DMUs. Finally, We present Illustrate example with two steps for proposed model that can be develop for more than two steps. تفاصيل المقالة
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  24 - Efficiency Evaluation of Economic Enterprise in Presence of Interval Undesirable and Negative Data
  MAHNAZ MAGHBOULI مهدی عینی فرهاد طاهر FATEMEH GHOMANJANI
  Data envelopment analysis (DEA) as a non-parametric method has covered a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that all input and o أکثر

  Data envelopment analysis (DEA) as a non-parametric method has covered a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that all input and output variables be known as semi positive. In many real situations, the presence of undesirable and even negative data are inevitable. In DEA literature there have been various approaches to enable DEA to deal with negative data. On the other hand, the structure of interval data has recently attracted considerable attention among DEA researchers. According to importance of interval data, this paper proposes a radial measure which permits the presence of undesirable and negative data with interval structure. The proposed model can evaluate the efficiency of all DMUs and leads to improve the inefficient unit with interval negative and undesirable data. To elucidate the details of the proposed method an illustrative example of a private bank in IRAN explores the applicability of the proposed method. تفاصيل المقالة
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  25 - A new robust optimization approach to most efficient formulation in DEA
  Reza Akhlaghi Mohsen Rostamy-Malkhalifeh Alireza Amirteimoori Sohrab Kordrostami
  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 inco أکثر

  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 تفاصيل المقالة
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  26 - EFFICIENCY MEASUREMENT OF NDEA WITH INTERVAL DATA
  S. Keikha- Javan M. Rostamy-Malkhalifeh
  Data envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many أکثر

  Data envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Among them are two or multi-stage models with serial or parallel structure often called network DEA models that are widely discussed in professional community in the last years. The exact known inputs and outputs are required in these DEA models. However, in the real world, the concern is systems with interval (bounded) data. When we incorporate such interval data into multi-stage DEA models, the resulting DEA model becomes a non-linear programming problem. In this study, we suggest an approach to measure the efficiency of series and parallel systems with interval data that preserves the linearity of DEA model. Also, the interval DEA models are proposed to measure the lower and upper bounds of the efficiency of each DMU with interval data. تفاصيل المقالة