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Journal articles on the topic 'Neutrosophic Data Envelopment Analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Edalatpanah, S. A. "Data envelopment analysis based on triangular neutrosophic numbers." CAAI Transactions on Intelligence Technology 5, no. 2 (2020): 94–98. http://dx.doi.org/10.1049/trit.2020.0016.

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2

Mohanta, Kshitish Kumar, and Oguz Toragay. "Enhanced Performance Evaluation Through Neutrosophic Data Envelopment Analysis Leveraging Pentagonal Neutrosophic Numbers." Journal of Operational and Strategic Analytics 1, no. 2 (2023): 70–80. http://dx.doi.org/10.56578/josa010204.

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3

Mohanta, Kshitish Kumar, Deena Sunil Sharanappa, and Abha Aggarwal. "A novel modified Khatter’s approach for solving Neutrosophic Data Envelopment Analysis." Croatian operational research review 14, no. 1 (2023): 15–28. http://dx.doi.org/10.17535/crorr.2023.0002.

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The evaluation of the performance of decision-making units (DMUs) that use comparable inputs to produce related outputs can be accomplished through a non-parametric linear programming (LP) technique called Data Envelopment Analysis (DEA). However, the observed data are occasionally imprecise, ambiguous, inadequate, and inconsistent which may result in incorrect decision-making when these criteria are ignored. Neutrosophic Set (NS) is an extension of fuzzy sets which is used to represent unclear, erroneous, missing, and wrong information. This paper proposes a neutrosophic version of the DEA mo

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4

Tapia, John Frederick D. "Evaluating negative emissions technologies using neutrosophic data envelopment analysis." Journal of Cleaner Production 286 (March 2021): 125494. http://dx.doi.org/10.1016/j.jclepro.2020.125494.

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5

Yang, Wei, Lulu Cai, Seyed Ahmad Edalatpanah, and Florentin Smarandache. "Triangular Single Valued Neutrosophic Data Envelopment Analysis: Application to Hospital Performance Measurement." Symmetry 12, no. 4 (2020): 588. http://dx.doi.org/10.3390/sym12040588.

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The foremost broadly utilized strategy for the valuation of the overall performance of a set of identical decision-making units (DMUs) that use analogous sources to yield related outputs is data envelopment analysis (DEA). However, the witnessed values of the symmetry or asymmetry of different types of information in real-world applications are sometimes inaccurate, ambiguous, inadequate, and inconsistent, so overlooking these conditions may lead to erroneous decision-making. Neutrosophic set theory can handle these occasions of data and makes an imitation of the decision-making procedure with

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6

Mao, Xinna, Zhao Guoxi, Mohammad Fallah, and S. A. Edalatpanah. "A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs." Mathematical Problems in Engineering 2020 (July 13, 2020): 1–8. http://dx.doi.org/10.1155/2020/7626102.

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Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may ad

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7

Jaberi Hafshjani, Mohammad, Seyyed Esmaeil Najafi, Farhad Hosseinzadeh Lotfi, and Seyyed Mohammad Hajimolana. "A Hybrid BSC-DEA Model with Indeterminate Information." Journal of Mathematics 2021 (April 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/8867135.

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Strategy is the main source of long-term growth for organizations, and if it is not successfully implemented, even if appropriate ones are adopted, the process is futile. The balanced scorecard which focuses on four aspects such as growth and learning, internal processes, customer, and financial is considered as a comprehensive framework for assessing performance and the progress of the strategy. Moreover, the data envelopment analysis is one of the best mathematical methods to compute the efficiency of organizations. The combination of these two techniques is a significant quantitative measur

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8

Rasinojehdehi, Reza, and Hadi Bagherzadeh Valami. "A comprehensive neutrosophic model for evaluating the efficiency of airlines based on SBM model of network DEA." Decision Making: Applications in Management and Engineering 6, no. 2 (2023): 880–906. http://dx.doi.org/10.31181/dma622023729.

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This research aims to provide an innovative framework for evaluating airline performance through the networking of decision-making units (DMUs) and neutrosophic data. As a consequence, we propose a comprehensive methodology for assessing the efficiency of these decision-making units. Network data envelopment analysis (DEA) models deal with measurements of relative efficiency of DMUs when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and

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9

Allen, R., and E. Thanassoulis. "Improving envelopment in data envelopment analysis." European Journal of Operational Research 154, no. 2 (2004): 363–79. http://dx.doi.org/10.1016/s0377-2217(03)00175-9.

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10

TONE, Kaoru. "Data Envelopment Analysis." Journal of Japan Society for Fuzzy Theory and Systems 8, no. 1 (1996): 11–14. http://dx.doi.org/10.3156/jfuzzy.8.1_11.

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11

Pereira, José Veiga. "Data Envelopment Analysis." Review of Business and Legal Sciences, no. 6 (July 5, 2017): 29. http://dx.doi.org/10.26537/rebules.v0i6.811.

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A avaliação das organizações e a deterntinação da performance obtida pelo exercício da gestão, tem sido uma preocupação constante de gestores e accionistas, embora com objectivos diversos. Nos dias de hoje, a questão coloca-se com maior acuidade quer pela competitividade acrescida quer pela dimensão e complexidade actual das empresas. Pretendemos com este trabalho fazer uma descrição da metodologia DEA - Data Envelopment Analysis - nas suas formulações iniciais mais simples. A metodologia do DEA, pretende obter uma medida única e simples de avaliação da eficiência, combinando um conjunto de ou

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12

Gregoriou, Greg N., and Joe Zhu. "Data Envelopment Analysis." Journal of Portfolio Management 33, no. 2 (2007): 120–32. http://dx.doi.org/10.3905/jpm.2007.674798.

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13

Wöber, Karl W. "Data Envelopment Analysis." Journal of Travel & Tourism Marketing 21, no. 4 (2007): 91–108. http://dx.doi.org/10.1300/j073v21n04_07.

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14

Wei, Quanling. "Data envelopment analysis." Chinese Science Bulletin 46, no. 16 (2001): 1321–32. http://dx.doi.org/10.1007/bf03183382.

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15

El-Mahgary, Sami. "Data Envelopment Analysis." OR Insight 8, no. 4 (1995): 15–22. http://dx.doi.org/10.1057/ori.1995.21.

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16

Molinero, Cecilio Mar, and David Woracker. "Data Envelopment Analysis:." OR Insight 9, no. 4 (1996): 22–28. http://dx.doi.org/10.1057/ori.1996.21.

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17

Ji, Yong-bae, and Choonjoo Lee. "Data Envelopment Analysis." Stata Journal: Promoting communications on statistics and Stata 10, no. 2 (2010): 267–80. http://dx.doi.org/10.1177/1536867x1001000207.

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18

Mescher, Markus, and Andreas Müller. "Data Envelopment Analysis." Controlling 14, no. 4-5 (2002): 299–300. http://dx.doi.org/10.15358/0935-0381-2002-4-5-299.

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19

Poddig, Thorsten, and Armin Varmaz. "Data Envelopment Analysis." Controlling 17, no. 10 (2005): 565–72. http://dx.doi.org/10.15358/0935-0381-2005-10-565.

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20

Färe, Rolf, and Valentin Zelenyuk. "Sequential data envelopment analysis." Annals of Operations Research 300, no. 1 (2021): 307–12. http://dx.doi.org/10.1007/s10479-020-03924-x.

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21

Schumacher, Christin. "Data Envelopment Analysis (DEA)." Controlling 25, no. 6 (2013): 336–37. http://dx.doi.org/10.15358/0935-0381_2013_6_336.

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22

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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23

Alp, Ihsan. "Possibilistic Data Envelopment Analysis." Mathematical and Computational Applications 7, no. 1 (2002): 5–14. http://dx.doi.org/10.3390/mca7010005.

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24

Bhat, Ramesh, Bharat Bhushan Verma, and Elan Reuben. "Data Envelopment Analysis (DEA)." Journal of Health Management 3, no. 2 (2001): 309–28. http://dx.doi.org/10.1177/097206340100300207.

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25

SENGUPTA, JATI K. "Dynamic data envelopment analysis." International Journal of Systems Science 27, no. 3 (1996): 277–84. http://dx.doi.org/10.1080/00207729608929214.

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26

Kuosmanen, T., and T. Post. "Quadratic data envelopment analysis." Journal of the Operational Research Society 53, no. 11 (2002): 1204–14. http://dx.doi.org/10.1057/palgrave.jors.2601432.

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27

Post, Thierry. "Transconcave data envelopment analysis." European Journal of Operational Research 132, no. 2 (2001): 374–89. http://dx.doi.org/10.1016/s0377-2217(00)00158-2.

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28

Ehrgott, Matthias, Allen Holder, and Omid Nohadani. "Uncertain Data Envelopment Analysis." European Journal of Operational Research 268, no. 1 (2018): 231–42. http://dx.doi.org/10.1016/j.ejor.2018.01.005.

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29

Ahn, Heinz, and Nadia Vazquez Novoa. "Balanced Data Envelopment Analysis." Controlling 30, no. 3 (2018): 62–68. http://dx.doi.org/10.15358/0935-0381-2018-3-62.

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30

Chen, Y.-W., M. Larbani, and Y.-P. Chang. "Multiobjective data envelopment analysis." Journal of the Operational Research Society 60, no. 11 (2009): 1556–66. http://dx.doi.org/10.1057/jors.2009.92.

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31

Sungmook, Lim. "Preface: data envelopment analysis." INFOR: Information Systems and Operational Research 56, no. 3 (2018): i—ii. http://dx.doi.org/10.1080/03155986.2018.1554935.

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32

Boussofiane, A., R. G. Dyson, and E. Thanassoulis. "Applied data envelopment analysis." European Journal of Operational Research 52, no. 1 (1991): 1–15. http://dx.doi.org/10.1016/0377-2217(91)90331-o.

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33

Agarwal, Shivi, Shiv Prasad Yadav, and S. P. Singh. "Sensitivity analysis in data envelopment analysis." International Journal of Operational Research 19, no. 2 (2014): 174. http://dx.doi.org/10.1504/ijor.2014.058948.

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34

Kuosmanen, T. "Data envelopment analysis with missing data." Journal of the Operational Research Society 60, no. 12 (2009): 1767–74. http://dx.doi.org/10.1057/jors.2008.132.

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35

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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36

Despotis, Dimitris K., and Yiannis G. Smirlis. "Data envelopment analysis with imprecise data." European Journal of Operational Research 140, no. 1 (2002): 24–36. http://dx.doi.org/10.1016/s0377-2217(01)00200-4.

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37

Khezrimotlagh, Dariush, Joe Zhu, Wade D. Cook, and Mehdi Toloo. "Data envelopment analysis and big data." European Journal of Operational Research 274, no. 3 (2019): 1047–54. http://dx.doi.org/10.1016/j.ejor.2018.10.044.

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38

Ahn, Heinz, Christoph Spang, and Nadia Vazquez Novoa. "Fehlanreize der Data Envelopment Analysis." Controlling 26, no. 3 (2014): 155–60. http://dx.doi.org/10.15358/0935-0381_2014_3_155.

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39

Homburg, Carsten. "Benchmarking durch Data Envelopment Analysis." WiSt - Wirtschaftswissenschaftliches Studium 29, no. 10 (2000): 583–88. http://dx.doi.org/10.15358/0340-1650-2000-10-583.

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40

Richter, Magnus, and Erik Borsch. "Effizienzmessung mittels Data Envelopment Analysis." WiSt - Wirtschaftswissenschaftliches Studium 46, no. 5 (2017): 20–26. http://dx.doi.org/10.15358/0340-1650-2017-5-20.

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41

GUPTA, JATI K. SEN. "CONTRIBUTIONS TO DATA ENVELOPMENT ANALYSIS." Cybernetics and Systems 28, no. 1 (1997): 79–98. http://dx.doi.org/10.1080/019697297126263.

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42

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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43

Desai, Anand. "Data envelopment analysis: A clarification." Evaluation & Research in Education 6, no. 1 (1992): 39–41. http://dx.doi.org/10.1080/09500799209533313.

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44

SAKOUVOGUI, KEKOURA. "Misspecication of Data Envelopment Analysis." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10032036.

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45

Ali, Agha Iqbal. "Data envelopment analysis: Computational issues." Computers, Environment and Urban Systems 14, no. 2 (1990): 157–65. http://dx.doi.org/10.1016/0198-9715(90)90020-t.

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46

Yan, Hong, and Quanling Wei. "Data envelopment analysis classification machine." Information Sciences 181, no. 22 (2011): 5029–41. http://dx.doi.org/10.1016/j.ins.2011.07.011.

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47

Land, Kenneth C., C. A. Knox Lovell, and Sten Thore. "Chance-constrained data envelopment analysis." Managerial and Decision Economics 14, no. 6 (1993): 541–54. http://dx.doi.org/10.1002/mde.4090140607.

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48

Sakouvogui, Kekoura. "Misspecification of data envelopment analysis." International Journal of Operational Research 45, no. 4 (2022): 467. http://dx.doi.org/10.1504/ijor.2022.128395.

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49

Emrouznejad, Ali. "Advances in data envelopment analysis." Annals of Operations Research 214, no. 1 (2014): 1–4. http://dx.doi.org/10.1007/s10479-014-1535-4.

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50

McCabe, Brenda, Viet Tran, and Joseph Ramani. "Construction prequalification using data envelopment analysis." Canadian Journal of Civil Engineering 32, no. 1 (2005): 183–93. http://dx.doi.org/10.1139/l04-073.

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Contractor prequalification is essential in most construction projects, and the process has been performed by many different methods in practice. Data envelopment analysis (DEA) had been recognized as a useful technique to prequalify contractors by assigning relative efficiency scores. Data envelopment analysis, however, usually requires a large amount of data and has not been fully developed to achieve reliable results. An enhanced contractor prequalification model using DEA was developed together with a methodology for determining a "practical frontier" of best contractors. The established p

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