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Journal articles on the topic 'Stochastic data envelopment analysis'

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Published: 5 June 2025

Last updated: 1 August 2025

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1

Banker, Rajiv D. "Stochastic Data Envelopment Analysis." Data Envelopment Analysis Journal 5, no. 2 (2021): 281–309. http://dx.doi.org/10.1561/103.00000038.

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2

Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical e

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3

Sihotang, Hengki Tamando, Syahril Efendi, Muhammad Zarlis, and Herman Mawengkang. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497–504. http://dx.doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical examp

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4

Ruggiero, John. "Data envelopment analysis with stochastic data." Journal of the Operational Research Society 55, no. 9 (2004): 1008–12. http://dx.doi.org/10.1057/palgrave.jors.2601779.

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5

Olesen, Ole B., and Niels Christian Petersen. "Stochastic Data Envelopment Analysis—A review." European Journal of Operational Research 251, no. 1 (2016): 2–21. http://dx.doi.org/10.1016/j.ejor.2015.07.058.

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6

Sengupta, Jati K. "Efficiency analysis by stochastic data envelopment analysis." Applied Economics Letters 7, no. 6 (2000): 379–83. http://dx.doi.org/10.1080/135048500351311.

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7

Abdullah, Dahlan, Hartono, and Cut Ita Erliana. "Hesitant Fuzzy-Stochastic Data Envelopment Analysis (HF-SDEA) Model for Benchmarking." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 94. http://dx.doi.org/10.30630/joiv.5.1.405.

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The Data Envelopment Analysis (DEA) method is a method commonly used in benchmarking. The Dynamic Data Envelopment Analysis (DDEA) method was proposed to improve the DEA method in the benchmarking process. The DDEA method proposed can determine the effectiveness of the Decision Making Unit (DMU). The disadvantage of the DDEA model is that it cannot handle problems that involve benchmarking for stochastic data. To improve the DDEA method, the Stochastic Data Envelopment Analysis (SDEA) method is proposed which can be used for benchmarking involving stochastic data. The SDEA method itself has we

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8

Desai, Anand, Samuel J. Ratick, and Arie P. Schinnar. "Data envelopment analysis with stochastic variations in data." Socio-Economic Planning Sciences 39, no. 2 (2005): 147–64. http://dx.doi.org/10.1016/j.seps.2004.01.005.

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9

Sengupta, Jati K. "Stochastic data envelopment analysis: a new approach." Applied Economics Letters 5, no. 5 (1998): 287–90. http://dx.doi.org/10.1080/758524402.

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10

Huang, Zhimin, and Susan X. Li. "Dominance stochastic models in data envelopment analysis." European Journal of Operational Research 95, no. 2 (1996): 390–403. http://dx.doi.org/10.1016/0377-2217(95)00293-6.

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11

Boyd, Taylor, Grace Docken, and John Ruggiero. "Outliers in data envelopment analysis." Journal of Centrum Cathedra 9, no. 2 (2016): 168–83. http://dx.doi.org/10.1108/jcc-09-2016-0010.

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Purpose The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error. Design/methodology/approach The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived. Findings This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simul

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12

Efendi, Syahril, N. A. Sutarman, Anton Abdulbasah Kamil, Marah Doly Nasution, and Herman Mawengkang. "Sample median approximation on stochastic data envelopment analysis." International Journal of Agile Systems and Management 13, no. 3 (2020): 279. http://dx.doi.org/10.1504/ijasm.2020.10031509.

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13

Nasution, Marah Doly, Herman Mawengkang, Anton Abdulbasah Kamil, Syahril Efendi, and N. A. Sutarman. "Sample median approximation on stochastic data envelopment analysis." International Journal of Agile Systems and Management 13, no. 3 (2020): 279. http://dx.doi.org/10.1504/ijasm.2020.109242.

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14

Banker, R. D., K. Kotarac, and L. Neralić. "Sensitivity and stability in stochastic data envelopment analysis." Journal of the Operational Research Society 66, no. 1 (2015): 134–47. http://dx.doi.org/10.1057/jors.2012.182.

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15

Pasaribu, Elvina. "Super Efficiency in Data Envelopment Analysis and Stochastic Frontier Analysis." International Journal for Research in Applied Science and Engineering Technology 6, no. 5 (2018): 1215–18. http://dx.doi.org/10.22214/ijraset.2018.5198.

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16

El-Demerdash, Basma E., Assem A. Tharwat, and Ihab A. A. El-Khodary. "A Unified Mathematical Model for Stochastic Data Envelopment Analysis." International Journal of Service Science, Management, Engineering, and Technology 12, no. 1 (2021): 127–41. http://dx.doi.org/10.4018/ijssmet.2021010108.

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Efficiency measurement is one aspect of organizational performance that managers are usually interested in determining. Data envelopment analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about the efficiency and performance of organizations and all sorts of functionally similar, relatively autonomous operating units. DEA models are either with a constant rate of return (CRS) or variable return to scale (VRS). Furthermore, the models could be input-oriented or output-oriented. In many real-life applications, observations are usually random in natu

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17

Wei, Bo-wen, Yi-yi Ma, and Ai-bing Ji. "Stage stochastic incremental data envelopment analysis models and applications." Socio-Economic Planning Sciences 95 (October 2024): 102056. http://dx.doi.org/10.1016/j.seps.2024.102056.

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18

Charles, Vincent, and Fabien Cornillier. "Value of the stochastic efficiency in data envelopment analysis." Expert Systems with Applications 81 (September 2017): 349–57. http://dx.doi.org/10.1016/j.eswa.2017.03.061.

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19

Lahdelma, Risto, and Pekka Salminen. "Stochastic multicriteria acceptability analysis using the data envelopment model." European Journal of Operational Research 170, no. 1 (2006): 241–52. http://dx.doi.org/10.1016/j.ejor.2004.07.040.

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20

SENGUPTA, JATI K. "Structural efficiency in stochastic models of data envelopment analysis†." International Journal of Systems Science 21, no. 6 (1990): 1047–56. http://dx.doi.org/10.1080/00207729008910432.

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21

Zhou, Yi, Lianshui Li, Ruiling Sun, Zaiwu Gong, Mingguo Bai, and Guo Wei. "Haze Influencing Factors: A Data Envelopment Analysis Approach." International Journal of Environmental Research and Public Health 16, no. 6 (2019): 914. http://dx.doi.org/10.3390/ijerph16060914.

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This paper investigates the meteorological factors and human activities that influence PM2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relation

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22

Teplova, T. V., T. V. Sokolova, and A. I. Haniev. "Portfolio constructions in the stock market based on data envelopment analysis and stochastic frontier analysis." Economics and Mathematical Methods 60, no. 2 (2024): 123–38. http://dx.doi.org/10.31857/s0424738824020102.

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The study compares the results of applying the parametric method of Stochastic Frontier Analysis (SFA) and the non-parametric Bias-corrected Data Envelopment Analysis (DEA) for forming integrated stock selection metrics in portfolios based on diverse financial and non-financial indicators of U.S. issuing companies. The authors implement a novel approach in which “input” and “output” indicators for both stochastic frontier analysis and data envelopment analysis models are pre-selected using regression analysis. Deviations of identified company indicators from median industry values are consider

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23

Ohoriemu, O. B., F. Z. Okwonu, and R. G. Mohammad. "Investigating the discriminating power and efficiency of Data Envelopment Analysis (DEA) model based on Stochastic Data Envelopment Analysis." Nigerian Journal of Science and Environment 24, no. 1 (2025): 1–12. https://doi.org/10.61448/njse231251.

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The problem associated with data envelopment analysis (DEA) is the lack of discrimination power among efficient decision-making units (DMUs), hence yielding many DMUs to be efficient. The issue is highlighted when the number of DMUs evaluated is significantly less than the number of inputs and outputs used in the evaluation. Therefore, other DEA models were proposed in the literature to overcome the discriminant power problems. However, the existing DEA models suffer from some other difficulties such as infeasible solutions for efficient DMUs, the non- uniqueness of the input-output weights, e

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24

Xia, Meimei. "Choquet-Integral-Based Data Envelopment Analysis with Stochastic Multicriteria Acceptability Analysis." Symmetry 14, no. 4 (2022): 642. http://dx.doi.org/10.3390/sym14040642.

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Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiencies of decision-making units (DMUs) by using a set of inputs and a set of outputs. However, traditional DEA models always assume that the inputs or outputs are independent of each other, which is unrealistic in practical problems. To reflect the interactions between inputs or outputs, the Choquet integral is employed in DEA models. The traditional DEA models are usually used to find some specific input and output weights of DMUs to optimize the efficiency score of DMUs, but the corresponding input and output

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25

Bagheri, Seyedeh Fatemeh, Alireza Amirteimoori, Sohrab Kordrostami, and Mansour Soufi. "Performance Analysis in Production Systems with Uncertain Data: A Stochastic Data Envelopment Analysis Approach." Complexity 2022 (October 12, 2022): 1–14. http://dx.doi.org/10.1155/2022/9198737.

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The problem of determining an optimal benchmark to inefficient decision-making units (DMUs) is an important issue in the field of performance analysis. Previous methods for determining the projection points of inefficient DMUs have only focused on one objective and other features have been ignored. This paper attempts to determine the best projection point for each DMU when the inputs and outputs data are in stochastic form and presents an alternative definition for the best projection by considering three main aspects: technical efficient, minimal cost, and maximal revenue as much as possible

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26

Tsionas, Mike G. "Optimal combinations of stochastic frontier and data envelopment analysis models." European Journal of Operational Research 294, no. 2 (2021): 790–800. http://dx.doi.org/10.1016/j.ejor.2021.02.003.

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27

Parmeter, Christopher F., and Valentin Zelenyuk. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis." Operations Research 67, no. 6 (2019): 1628–58. http://dx.doi.org/10.1287/opre.2018.1831.

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28

Olesen, O. B., and N. C. Petersen. "Incorporating quality into data envelopment analysis: a stochastic dominance approach." International Journal of Production Economics 39, no. 1-2 (1995): 117–35. http://dx.doi.org/10.1016/0925-5273(94)00065-i.

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29

Retzlaff-Roberts, Donna L., and Richard C. Morey. "A goal-programming method of stochastic allocative data envelopment analysis." European Journal of Operational Research 71, no. 3 (1993): 379–97. http://dx.doi.org/10.1016/0377-2217(93)90348-q.

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30

Sengupta, Jati K. "Data envelopment analysis for efficiency measurement in the stochastic case." Computers & Operations Research 14, no. 2 (1987): 117–29. http://dx.doi.org/10.1016/0305-0548(87)90004-9.

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31

Ebrahimnejad, Ali, Seyed Hadi Nasseri, and Omid Gholami. "Fuzzy stochastic Data Envelopment Analysis with application to NATO enlargement problem." RAIRO - Operations Research 53, no. 2 (2019): 705–21. http://dx.doi.org/10.1051/ro/2018075.

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Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of Decision Making Units (DMUs) with multiple deterministic inputs and multiple outputs. However, in real-world problems, the observed values of the input and output data are often vague or random. Indeed, Decision Makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Hence, we formulate a new DEA model to deal with fuzzy stochastic DEA models. The contributions of the present study are fivefold: (1) We formulate a deterministic linear mode

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32

Khodabakhshi, M. "Estimating most productive scale size with stochastic data in data envelopment analysis." Economic Modelling 26, no. 5 (2009): 968–73. http://dx.doi.org/10.1016/j.econmod.2009.03.002.

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33

Mahmood, Tariq, Ejaz Ghani, and Musleh Ud Din . "Are Our Export-Oriented Industries Technically More Efficient?" Pakistan Development Review 54, no. 2 (2015): 97–121. http://dx.doi.org/10.30541/v54i2pp.97-121.

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This paper makes a comparison of technical efficiency scores between groups of exporting and non-exporting industries. Using data from Census of Manufacturing Industries in Pakistan (2005-06), technical efficiency scores of 102 large scale manufacturing industries are estimated. Stochastic Frontier Analysis as well as Data Envelopment Analysis technique are used to estimate technical efficiency scores. In Stochastic Frontier Analysis Translog and Cobb-Douglass Production Functions are specified, whereas in Data Envelopment Analysis technique, efficiency scores are computed under the assumption

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34

Deng, Qiang. "A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis." Mathematics 12, no. 18 (2024): 2913. http://dx.doi.org/10.3390/math12182913.

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This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates tha

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35

Mourad, Nahia. "Second-order conic programming for data envelopment analysis models." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 2 (2022): 487–99. https://doi.org/10.21533/pen.v10.i2.625.

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Data envelopment analysis (DEA) is a widely used benchmarking technique. Its strength stems from the fact that it can include several inputs and outputs of not necessarily the same type to evaluate efficiency scores. Indeed, the aforesaid method is based on mathematical optimization. This paper constructs a second-order conic optimization problem unifying several DEA models. Moreover, it presents an algorithm that solves the former problem, and provides a MATLAB function associated with it. As far as known, no MATLAB function solves DEA models. Among different types of DEA, this function can h

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36

Post, Thierry. "Performance Evaluation in Stochastic Environments Using Mean-Variance Data Envelopment Analysis." Operations Research 49, no. 2 (2001): 281–92. http://dx.doi.org/10.1287/opre.49.2.281.13529.

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37

Cooper, William W., H. Deng, Zhimin Huang, and Susan X. Li. "Chance constrained programming approaches to congestion in stochastic data envelopment analysis." European Journal of Operational Research 155, no. 2 (2004): 487–501. http://dx.doi.org/10.1016/s0377-2217(02)00901-3.

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38

Cooper, W. W., and K. Tone. "Measures of inefficiency in data envelopment analysis and stochastic frontier estimation." European Journal of Operational Research 99, no. 1 (1997): 72–88. http://dx.doi.org/10.1016/s0377-2217(96)00384-0.

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39

Li, Susan X. "Stochastic models and variable returns to scales in data envelopment analysis." European Journal of Operational Research 104, no. 3 (1998): 532–48. http://dx.doi.org/10.1016/s0377-2217(97)00002-7.

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40

Khodabakhshi, M. "Super-efficiency in stochastic data envelopment analysis: An input relaxation approach." Journal of Computational and Applied Mathematics 235, no. 16 (2011): 4576–88. http://dx.doi.org/10.1016/j.cam.2010.03.023.

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41

Khodabakhshi, M., and M. Asgharian. "An input relaxation measure of efficiency in stochastic data envelopment analysis." Applied Mathematical Modelling 33, no. 4 (2009): 2010–23. http://dx.doi.org/10.1016/j.apm.2008.05.006.

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42

Watson, John, J. Wickramanayke, and I. M. Premachandra. "The value of Morningstar ratings: evidence using stochastic data envelopment analysis." Managerial Finance 37, no. 2 (2011): 94–116. http://dx.doi.org/10.1108/03074351111103659.

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43

Pillai N, Vijayamohanan, and AM Narayanan. "Measuring Energy Efficiency in Kerala: Data Envelopment Analysis." Journal of Biomedical Engineering and Medical Imaging 6, no. 4 (2019): 16–28. http://dx.doi.org/10.14738/jbemi.64.7629.

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Traditionally, there are two basically reciprocal energy efficiency Indicators: one, in terms of energy intensity, that is, energy use per unit of activity output, and the other, in terms of energy productivity, that is, activity output per unit of energy use. The enquiry that has proceeded from the problems associated with this method of a single energy input factor in terms of productivity has led to multi-factor productivity analysis. We have here two approaches: parametric and non-parametric. Parametric approach famously includes two methods: the erstwhile popular total factor energy produ

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44

Azadi, Majid, and Reza Farzipoor Saen. "Developing a New Theory of Integer-Valued Data Envelopment Analysis for Supplier Selection in the Presence of Stochastic Data." International Journal of Information Systems and Supply Chain Management 7, no. 3 (2014): 80–103. http://dx.doi.org/10.4018/ijisscm.2014070104.

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Supplier selection has a strategic importance for every company. Hybrid integer data is one of the models in data envelopment analysis (DEA). In many real world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained hybrid integer data envelopment analysis (CCHIDEA) model is developed and also its deterministic equivalent which is a nonlinear program is derived. Furthermore, it is shown that the deterministic equivalent of the CCHI

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45

Kuosmanen, Timo, and Natalia Kuosmanen. "Efficiency analysis of Finnish crop farms by Stochastic Nonparametric Envelopment of Data (StoNED)." Suomen Maataloustieteellisen Seuran Tiedote, no. 26 (January 31, 2010): 1–6. http://dx.doi.org/10.33354/smst.75770.

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A new two-stage approach to modeling the nutrient emissions of crop farms is developed. In the first stage, we estimate the classic production function using farm-level data. We apply the stochastic nonparametric envelopment of data (StoNED) method that combines the axiomatic, nonparametric modeling of the production technology with a stochastic, probabilistic treatment of inefficiency and noise. Given the estimated input saving and output expansion potential for each farm in the sample, we estimate the impacts of the decreased fertilizer use and the increased crop yield on the nutrient balanc

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46

Hossain, Md Kamrul, Anton Abdulbasah Kamil, Adli Mustafa, and Md Azizul Baten. "Efficiency in the Worst Production Situation Using Data Envelopment Analysis." Advances in Decision Sciences 2013 (March 27, 2013): 1–9. http://dx.doi.org/10.1155/2013/354509.

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Data envelopment analysis (DEA) measures relative efficiency among the decision making units (DMU) without considering noise in data. The least efficient DMU indicates that it is in the worst situation. In this paper, we measure efficiency of individual DMU whenever it losses the maximum output, and the efficiency of other DMUs is measured in the observed situation. This efficiency is the minimum efficiency of a DMU. The concept of stochastic data envelopment analysis (SDEA) is a DEA method which considers the noise in data which is proposed in this study. Using bounded Pareto distribution, we

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47

Abidin, Zaenal, R. Mahelan Prabantarikso, Edian Fahmy, and Amabel Nabila. "Comperative Efficiency using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) in the Banking Industry." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (November 2, 2023): 109–20. http://dx.doi.org/10.37394/23207.2024.21.10.

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This study’s objective is to employ data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to investigate the efficiency accomplishments of Indonesian commercial banking from 2018 to 2019. The first method of measuring efficiency employing a non-parametric data envelopment analysis (DEA) technique reveals that the average efficiency of 71 banks fell from 2018 (0.82) to 2019 (0.81). According to DEA findings, major banks outperform small banks on average. According to the approximated SFA Cobb-Douglas (CD) function, interest expenditure and labor expense have a positive and cons

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48

Aydin, Nezir, and Gökhan Yurdakul. "Analyzing the efficiency of bank branches via novel weighted stochastic imprecise data envelopment analysis." RAIRO - Operations Research 55, no. 3 (2021): 1559–78. http://dx.doi.org/10.1051/ro/2021067.

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As of 21st century, the terms of efficiency and productivity have become notions which dwells on both business and academic world more frequently compared to past. It is known that it is hard to increase the efficiency and productivity of both production and service systems. In this study, the efficiency analysis of the branches of a bank was conducted. Furthermore, a Weighted Stochastic Imprecise Data Envelopment Analysis (WSIDEA), which is a new approach developed based on Data Envelopment Analysis (DEA), was proposed. Efficiency levels and results of decision-making units were examined acco

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49

Udhayakumar, A., V. Charles, and Mukesh Kumar. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems." Omega 39, no. 4 (2011): 387–97. http://dx.doi.org/10.1016/j.omega.2010.09.002.

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50

Lozano, Sebastián, and Ester Gutiérrez. "Data envelopment analysis of mutual funds based on second-order stochastic dominance." European Journal of Operational Research 189, no. 1 (2008): 230–44. http://dx.doi.org/10.1016/j.ejor.2007.04.014.

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