Multiplier Models in Stochastic DEA

Mirbolouki, Mahnaz; Behzadi, Mohammad Hassan; Korzaledin, Mustafa

doi:10.5899/2014/dea-00044

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…In addition, basic DEA model is not accurate when faces with risk [25]; Wu and Lee [27]). The main advantage of SDEA is an accurate prediction in future decisions [17].…”

Section: Stochastic Deamentioning

confidence: 99%

Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis

Azadeh

Haghighi

Zarrin

et al. 2015

International Journal of Electrical Power & Energy Systems

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a b s t r a c tThis paper introduces an approach based on stochastic data envelopment analysis (SDEA) for performance assessment of electricity distribution units. A new approach is applied for assessment of Iranian distribution units from 2001 to 2011 in this paper. There are usually incomplete and stochastic data or lack of data with respect to electricity distribution companies. Due to lack of information about some parameters, theory of probability is imported to the model. Different Iranian distribution units are considered as decision making units (DMUs). Network length, transport capacity and the number of employees are chosen as inputs while number of customers and total electricity sales are chosen as stochastic outputs. Then, the best electricity distributions units are selected with respect to efficiency scores in stochastic environment. Also, SDEA model is performed for each input, separately to identify the most important input indicators by comparing the results of associated efficiencies with SDEA model. The empirical results show that network length is the most important and influential input factor in this particular case study. To the best of our knowledge this is the first paper that examines stochastic outputs for assessment of electricity distribution units by SDEA in Iran.

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“…In addition, basic DEA model is not accurate when faces with risk [25]; Wu and Lee [27]). The main advantage of SDEA is an accurate prediction in future decisions [17].…”

Section: Stochastic Deamentioning

confidence: 99%

Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis

Azadeh

Haghighi

Zarrin

et al. 2015

International Journal of Electrical Power & Energy Systems

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Stochastic Data Envelopment Analysis in Measuring the Efficiency of Electricity Distribution Companies

Yenioğlu

Toklu

2022

Advances in Computer and Electrical Engineering

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Performance benchmarking of electricity distribution markets is essential for improving industry performance parameters. In a benchmarking study, the most important problem is that regulators often do not have accurate, specific, and sufficient information to determine current input use to achieve expected amount of output. The study combines statistical symmetric error structure with stochastic chance constrained DEA models and compares deterministic data envelopment analysis (DEA) models with stochastic chance-constrained DEA models within random input and output variables. The proposed models were applied on Turkey's electricity distribution units for assessment of energy efficiency. Study revealed that the results obtained with random data softened efficiency frontier. This study contains symmetric error structure and random inputs and outputs for performance benchmarking of electricity distribution markets by stochastic data envelopment analysis within a symmetric error structure in Turkey.

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Multiplier Models in Stochastic DEA

Cited by 2 publications

References 11 publications

Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis

Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis

Stochastic Data Envelopment Analysis in Measuring the Efficiency of Electricity Distribution Companies

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