An additive model approach for estimating returns to scale in imprecise data envelopment analysis

Khodabakhshi, Mohammad; Gholami, Y.; Kheirollahi, Hooshang

doi:10.1016/j.apm.2009.08.011

Cited by 51 publications

(22 citation statements)

References 27 publications

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“…To remove this weakness in the conventional DEA models, some authors incorporated stochastic input and output variations into the DEA. See, for example, Huang and Li (1996) and Cooper et al (2004), Khodabakhshi and Asgharian (2009), Khodabakhshi (2009), and Khodabakhshi et al (2009aKhodabakhshi et al ( , 2009b among others. In what follows, stochastic version of the output oriented super-efficiency model is introduced which allows for the possibility of stochastic variations in input-output data.…”

Section: Stochastic Output Oriented Super-efficiency Modelmentioning

confidence: 97%

An output oriented super-efficiency measure in stochastic data envelopment analysis: Considering Iranian electricity distribution companies

Khodabakhshi

2010

Computers & Industrial Engineering

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Section: Stochastic Output Oriented Super-efficiency Modelmentioning

confidence: 97%

An output oriented super-efficiency measure in stochastic data envelopment analysis: Considering Iranian electricity distribution companies

Khodabakhshi

2010

Computers & Industrial Engineering

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“…The last column of Step 2: From Models (18), (21), (22) and (33), we can obtain the directional RTS to the "right" and "left" of each DMU. For comparison purposes, we set the direction of outputs as In Table 3, I, C and D denote increasing, constant and decreasing directional RTS, respectively.…”

Section: A Case Studymentioning

confidence: 99%

“…case, we cannot find a feasible solution in Model(22) when 0 left t  is a small positive constant. Thus, we provide the following Definition 12 to address the left-hand directional RTS of  …”

mentioning

confidence: 99%

Estimating directional returns to scale in DEA

Yang

Liu

2017

INFOR: Information Systems and Operational Research

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Data envelopment analysis (DEA) is one of the most commonly used methods to estimate the returns to scale (RTS) of the public sector (e.g., research institutions). Existing studies are all based on the traditional definition of RTS in economics and assume that multiple inputs and outputs change in the same proportion, which is the starting point to determine the qualitative and quantitative features of RTS of decision making units (DMUs). However, for more complex products, such as the scientific research in institutes, changes of various types of inputs or outputs are often not in proportion. Therefore, the existing definition of RTS in the framework of DEA method may not meet the need to estimate the RTS of research institutions with multiple inputs and outputs. This paper proposes a definition of directional RTS in the DEA framework and estimates the directional RTS of research institutions using DEA models. Further in-depth analysis is conducted for an illustrative example of 16 basic research institutes in Chinese Academy of Sciences (CAS) in 2010.* 00 , XY  of Model (22). This fact contradicts the supposition that the maximal optimal objective function    00 , XY of Model (28) is unbounded. Q.E.D.

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“…Several solution approaches have been developed for fuzzy DEA models, which include: 1) the defuzzification approach (Ghasemi, Ignatius, & Davoodi, 2014a;Hasuike, 2011;Wang & Chin, 2011), 2) the α-level based approach (Azadeh, Moghaddam, Asadzadeh, & Negahban, 2011;Azadeh, Sheikhalishahi, & Asadzadeh, 2011;Muren, Ma, & Cui, 2012;Puri & Yadav, 2012;Zerafat Angiz L, Emrouznejad, & Mustafa, 2010), 3) fuzzy ranking (Bagherzadeh valami, 2009;Guo & Tanaka, 2001;Hatami-Marbini, Saati, & Tavana, 2011b;Hatami-Marbini, Tavana, & Ebrahimi, 2011c;Soleimanidamaneh, 2009), 4) the possibility approach (Khodabakhshi, Gholami, & Kheirollahi, 2010;Lertworasirikul, Fang, Joines, & Nuttle, 2003), 5) fuzzy arithmetic (Wang, Greatbanks, & Yang, 2005;Wang, Luo, & Liang, 2009), and 6) the fuzzy random/type-2 fuzzy set (Qin & Liu, 2010;Qin, Liu, & Liu, 2011;Qin, Liu, Liu, & Wang, 2009). Fuzzy ranking and α-cut approaches are the most popular as outlined in a survey on fuzzy DEA literature (Hatami-Marbini et al, 2011a).…”

Section: Introductionmentioning

confidence: 99%

A fuzzy expected value approach under generalized data envelopment analysis

Ghasemi

Ignatius

Lozano

et al. 2015

Knowledge-Based Systems

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A fuzzy expected value approach under generalized data envelopment analysisA B S T R A C T Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models -fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models -and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

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An additive model approach for estimating returns to scale in imprecise data envelopment analysis

Cited by 51 publications

References 27 publications

An output oriented super-efficiency measure in stochastic data envelopment analysis: Considering Iranian electricity distribution companies

An output oriented super-efficiency measure in stochastic data envelopment analysis: Considering Iranian electricity distribution companies

Estimating directional returns to scale in DEA

A fuzzy expected value approach under generalized data envelopment analysis

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