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

Khodabakhshi, Mohammad

doi:10.1016/j.cie.2010.01.009

Cited by 40 publications

(21 citation statements)

References 30 publications

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“…Pergelova [5] used both DEA and stochastic frontier analysis (SFA) to evaluate the efficiency of marketing automobiles in Spain with respect to offline and online delivery methods. Khodabakhshi [6] used a chance constrained DEA model to develop a stochastic super-efficiency model for the purpose of evaluating the efficiency of 17 electrical distribution companies in Iran. Khodabakhshi, Asgharian, and Gregoriou [7] next applied their stochastic super-efficiency DEA model to the efficiency assessment of United States public banks and thrifts CEOs.…”

Section: A Data Envelopment Analysismentioning

confidence: 99%

The Educational Efficiency Evaluation Framework: By Using DEA Model and CA Method

Yuan¹,

Shan²

2016

IJIET

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Section: A Data Envelopment Analysismentioning

confidence: 99%

The Educational Efficiency Evaluation Framework: By Using DEA Model and CA Method

Yuan¹,

Shan²

2016

IJIET

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“…Stochastic input and output variations of DEA have been studied by many researchers including: Land et al (1993), Olesen and Petersen (1995), Huang and Li (1996), Li (1998), Morita and Seiford (1999), Cooper et al (1996Cooper et al ( , 1998Cooper et al ( , 2002Cooper et al ( , 2004, Bruni et al (2009, Khodabakhshi and Asgharian (2009), Khodabakhshi (2009, 2010, Wu and Lee (2010), Khodabakhshi (2011) andHosseinzadeh Lotfi et al (2012). Land et al (1993) exploited the CCP proposed by Charnes and Cooper (1959) in order to compute efficiency in the face of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Khodabakhshi (2009) studied the most productive scale size with stochastic data in DEA and Khodabakhshi et al (2010) developed an input-oriented super-efficiency measure in stochastic DEA. Khodabakhshi (2011) later proposed the superefficiency concept based on an input relaxation model in stochastic DEA and Khodabakhshi (2010) used stochastic data with the concept of CCP to develop an output-oriented superefficiency DEA model. Azadi and Farzipoor Saen (2011) proposed a chance-constrained DEA method to assist the decision makers to identify the most appropriate third-party reverse logistics providers in the presence of both dual-role factors and stochastic data.…”

Section: Introductionmentioning

confidence: 99%

A new chance-constrained DEA model with birandom input and output data

Tavana

Shiraz

Hatami-Marbini

2014

Journal of the Operational Research Society

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The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-constrained DEA model with birandom input and output data. A super-efficiency model with birandom constraints is formulated and a non-linear deterministic equivalent model is obtained to solve the super-efficiency model. The non-linear model is converted into a model with quadratic constraints to solve the non-linear deterministic model. Furthermore, a sensitivity analysis is performed to assess the robustness of the proposed super-efficiency model. Finally, two numerical examples are presented to demonstrate the applicability of the proposed chance-constrained DEA model and sensitivity analysis.

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“…Stochastic output and input variation has been commonly used in DEA (Cooper et al, 2004;Huang and Li, 1996;Asgharian et al, 2010;Khodabakhshi, 2010). Cooper et al (2004) showed how to identify congestion with deterministic models rather than their stochastic counterparts under suitable assumptions.…”

Section: Introductionmentioning

confidence: 99%

Optimistic and pessimistic performance and congestion analysis in fuzzy data envelopment analysis

Khodabakhshi

Tavana

Abootaleb

2016

IJLSM

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The notion of input congestion in data envelopment analysis (DEA) is analogous to the 'law of diminishing returns' in the classical economic theory of production which states that if a single input is increased while other inputs are held constant, the marginal product of the variable input diminishes. Congestion has been an under-researched topic in economic theory especially when there is a need for augmenting inputs to serve important objectives besides output maximisation. We propose a fuzzy DEA model and represent the imprecise and ambiguous input and output data with fuzzy numbers. We solve the model with an α-cut approach and obtain the value of input congestion for the optimistic and pessimistic cases. The fundamental idea in this paper is to transform the fuzzy DEA model into a crisp linear programming 2 M Khodabakhshi et al. model using the α-cut approach. Two auxiliary crisp models are solved to obtain optimistic and pessimistic values of congestion for evaluating the decision-making units (DMUs). We use a numerical example from the literature to demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms.

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

Cited by 40 publications

References 30 publications

The Educational Efficiency Evaluation Framework: By Using DEA Model and CA Method

The Educational Efficiency Evaluation Framework: By Using DEA Model and CA Method

A new chance-constrained DEA model with birandom input and output data

Optimistic and pessimistic performance and congestion analysis in fuzzy data envelopment analysis

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