IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v268y2018i1p231-242.html
   My bibliography  Save this article

Uncertain Data Envelopment Analysis

Author

Listed:
  • Ehrgott, Matthias
  • Holder, Allen
  • Nohadani, Omid

Abstract

Data Envelopment Analysis (DEA) is a nonparametric, data driven method to conduct relative performance measurements among a set of decision making units (DMUs). Efficiency scores are computed based on assessing input and output data for each DMU by means of linear programming. Traditionally, these data are assumed to be known precisely. We instead consider the situation in which data is uncertain, and in this case, we demonstrate that efficiency scores increase monotonically with uncertainty. This enables inefficient DMUs to leverage uncertainty to counter their assessment of being inefficient.

Suggested Citation

  • Ehrgott, Matthias & Holder, Allen & Nohadani, Omid, 2018. "Uncertain Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 268(1), pages 231-242.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:231-242
    DOI: 10.1016/j.ejor.2018.01.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718300055
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.01.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Omid Nohadani & Kwong Meng Teo, 2010. "Nonconvex Robust Optimization for Problems with Constraints," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 44-58, February.
    2. Dimitris Bertsimas & Aurélie Thiele, 2006. "A Robust Optimization Approach to Inventory Theory," Operations Research, INFORMS, vol. 54(1), pages 150-168, February.
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    4. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    5. Meng Zhang & Jin-chuan Cui, 2016. "The extension and integration of the inverse DEA method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1212-1220, September.
    6. Emrouznejad, Ali & Parker, Barnett R. & Tavares, Gabriel, 2008. "Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 151-157, September.
    7. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huang, Hongyun & Mo, Renbian & Chen, Xingquan, 2021. "New patterns in China's regional green development: An interval Malmquist–Luenberger productivity analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 161-173.
    2. Kiani Mavi, Reza & Kiani Mavi, Neda, 2021. "National eco-innovation analysis with big data: A common-weights model for dynamic DEA," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    3. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    4. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.
    5. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    6. Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.
    7. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    2. Xiaoyin Hu & Jianshu Li & Xiaoya Li & Jinchuan Cui, 2020. "A Revised Inverse Data Envelopment Analysis Model Based on Radial Models," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    3. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    4. Kwon, He-Boong & Lee, Jooh, 2019. "Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms," International Journal of Production Economics, Elsevier, vol. 211(C), pages 1-14.
    5. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    6. Tran, Trung Hieu & Mao, Yong & Nathanail, Paul & Siebers, Peer-Olaf & Robinson, Darren, 2019. "Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis," Omega, Elsevier, vol. 85(C), pages 156-165.
    7. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    8. Chiang Kao & Shiang-Tai Liu, 2022. "Stochastic efficiencies of network production systems with correlated stochastic data: the case of Taiwanese commercial banks," Annals of Operations Research, Springer, vol. 315(2), pages 1151-1174, August.
    9. Pinto, Claudio, 2019. "Model and measure the relative efficiency of a four-stage production process. An NDEA multiplier relational model under different systems of resource distribution preferences between sub-processes," MPRA Paper 92617, University Library of Munich, Germany.
    10. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    11. Łukasz Brzezicki, 2020. "Przegląd badań dotyczących efektywności i produktywności polskiego szkolnictwa wyższego, prowadzonych za pomocą metody DEA i indeksu Malmquista," Post-Print hal-04414162, HAL.
    12. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    13. S. Lozano & L. Calzada-Infante & B. Adenso-Díaz & S. García, 2019. "Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 609-629, August.
    14. Maria Teresa Balaguer‐Coll & Isabel Narbón‐Perpiñá & Jesús Peiró‐Palomino & Emili Tortosa‐Ausina, 2022. "Quality of government and economic growth at the municipal level: Evidence from Spain," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 96-124, January.
    15. Łukasz Brzezicki, 2020. "The Efficiency of Public and Private Higher Education Institutions in Poland," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 33-51.
    16. Dimitrios Giokas & Nicolaos Eriotis & Ioannis Dokas, 2015. "Efficiency and productivity of the food and beverage listed firms in the pre-recession and recessionary periods in Greece," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1927-1941, April.
    17. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    18. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    19. Hisham Alidrisi & Mehmet Emin Aydin & Abdullah Omer Bafail & Reda Abdulal & Shoukath Ali Karuvatt, 2019. "Monitoring the Performance of Petrochemical Organizations in Saudi Arabia Using Data Envelopment Analysis," Mathematics, MDPI, vol. 7(6), pages 1-16, June.
    20. Gupta, Maansi & Bolia, Nomesh B., 2020. "Efficiency measurement of Indian high courts using DEA: A policy perspective," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1372-1393.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:231-242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.