2014
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Presenting a Hybrid Approach based on Twostage Data Envelopment Analysis to Evaluating Organization Productivity
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Measuring the performance of a production system has been an important task in management for purposes of control, planning, etc. Lord Kelvin said :“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind.” Hence, management knowledge is measurable science and if we can’t measure each subject we can’t control it so management is impossible. We know, the major criteria performance is productivity and we should be able to measure it.
Data Envelopment Analysis (DEA), as an evaluation method, can estimate the relative efficiency of organizations systematically. The efficiency of an organization can be benchmarked by using DEA. DEA presents a model for evaluating the performance of a set of comparable decision making units (DMUs). In this paper we developed a new model for calculating productivity with Twostage data envelopment analysis.
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291
302


E.
Najafi
Department of Industrial Engineering, Islamic Azad University  Science and Research Branch, Tehran, Iran.
Department of Industrial Engineering, Islamic
Iran


M.
Fallah
Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Industrial Engineering, South
Iran


S.
Hamed
Department of Industrial Engineering, Sharif University, Tehran, Iran.
Department of Industrial Engineering, Sharif
Iran
Data envelopment analysis (DEA)
Productivity
measuring performance
TwoStage DEA
Effectiveness
Prioritizing Potential Risks based on Failure Mode and Effects Analysis Using Data Envelopment Analysis: a case study
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2
The need to establish power plants to supply the required electricity of the country has been rising due to increasing demand levels as well as the lack of governmental resources. Furthermore, the available traditional attitude in performing and conducting power plant projects has made government seek a modern attitude. This paper tries to use the topics that have been employed in automotive industry of the company for many years to detect and analyse failure reasons as well as the solutions that are suggested to prevent and improve them. The present study has used Failure Mode & Effects Analysis along with Data Envelopment Analysis to evaluate 17 failure modes of South Esfahan Power Plant. Severity, Occurrence and Detection are 3 failure factors considered in this study. Failure mode dealing with Scaling on rotor and diaphragms blades has the highest importance with the highest Risk Priority Number,Severity and its first grade.
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303
314


B.
Nasr Esfahani
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
Department of Industrial Engineering, Najafabad
Iran


H.
Shirouyehzad
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
Department of Industrial Engineering, Najafabad
Iran


F.
Mokhatab Rafiei
Department of Industrial Engineering, Isfahan University of Technology, Esfahan, Iran
Department of Industrial Engineering, Isfahan
Iran
Evaluation
Risk
Failure mode and effects analysis (FMEA)
Data envelopment analysis (DEA)
Malmquist Productivity Index for Multi Time Periods
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2
The performance of a decision making unit (DMU) can be evaluated in either acrosssectional or a timeseries manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. The Malmquist productivity index (MPI) evaluates the change in efficiency of a DMU between two time periods. It is defined as the product of the Catchup and Frontiershift terms. In this paper, we study the Malmquist productivity index of a DMU between several time periods.
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315
322


Y.
Jafari
Department of Mathematics, Shabestar Branch, Islamic Azad University, Shabestar, Iran.
Department of Mathematics, Shabestar Branch,
Iran
Data Envelopment Analysis
Malmquist Productivity Index
Curve fitting
A Russell Measure for Modeling Environmental Performance
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Data Envelopment Analysis (DEA) has been long employed as a popular methodology to evaluate the performance of various production activities with multiple inputs and outputs. However, an important issue is that the production process in the real world inevitably generates undesirable outputs (like wastes and pollutants) along with desirable outputs. Therefore, the undesirable outputs should be included into the environmental performance evaluation. This study surveys the two technologies which is available in the DEA literature for modelling environmental performance under weak disposability assumption of good and bad outputs. Then, it attempts to present a Russell measure that incorporates both desirable and undesirable outputs. To illustrate the use of the proposed method, an empirical application corresponding to 31 administrative regions of China is provided and interpreted.
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323
332


H.
Zare Haghighi
Department of Mathematics, Science and Research Branch, Islamic Azad University,
Tehran, Iran
Department of Mathematics, Science and Research
Iran


M.
RostamyMalkhalifeh
Department of Mathematics, Science and Research Branch, Islamic Azad University,
Tehran, Iran
Department of Mathematics, Science and Research
Iran
Data Envelopment Analysis
Environmental performance
Undesirable Output
The Calculation of Unit's Efficiency by Using the Interval
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2
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 which may be less than or equal to one is obtained. Given the possible large number of efficiency units for evaluating units, we use various methods of ranking. span style="fontfamily: Cambria Math;fontsize:8pt;color:rgb(0,0,0);fontstyle:normal;fontvariant:normal L1 norm is one of the methods of ranking. This method has been used for categorical data. In this paper, we assume data as interval and introducespan style="fontfamily: Cambria Math;fontsize:8pt;color:rgb(0,0,0);fontstyle:normal;fontvariant:normal L1 norm andrun it on a single example.
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333
341


B.
Babazadeh
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,
Saveh, Iran
Department of Industrial Engineering, Science
Iran


E.
Najafi
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,
Tehran, Iran
Department of Industrial Engineering, Science
Iran


M.
AhadzadehNamin
Department of mathematics, ShahreQodsBranch, Islamic Azad University, Tehran, Iran
Department of mathematics, ShahreQodsBranch,
Iran


Y.
jafari
Department of Mathematics, Shabestar Branch, Islamic Azad University, Shabestar, Iran
Department of Mathematics, Shabestar Branch,
Iran


Z.
Ebrahimi
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,
Saveh, Iran
Department of Industrial Engineering, Science
Iran
DEA
Ranking
Interval data
L1norm
The Efficiency of MSBM Model with Imprecise Data (Interval)
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2
Data Envelopment Analysis (DEA) is a mathematical programmingbased 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 decisionmaking units with negative data. The Modified Slack Based Measure (MSBM) model is from collective models family. This modified model is based on slackbased 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 decisionmaking units is evaluated.
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343
350


F.
seyed Esmaeili
Department of Mathematics, Islamic Azad University, South Tehran Branch, Tehran, Iran
Department of Mathematics, Islamic Azad University
Iran
Data Envelopment Analysis
modified model
Interval data
evaluating the efficiency of negative data