IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v43y2022i1d10.1007_s10878-021-00765-7.html
   My bibliography  Save this article

A modified DEA cross efficiency method with negative data and its application in supplier selection

Author

Listed:
  • Mehdi Soltanifar

    (Islamic Azad University)

  • Hamid Sharafi

    (Islamic Azad University)

Abstract

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

Suggested Citation

  • Mehdi Soltanifar & Hamid Sharafi, 2022. "A modified DEA cross efficiency method with negative data and its application in supplier selection," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 265-296, January.
    • Handle: RePEc:spr:jcomop:v:43:y:2022:i:1:d:10.1007_s10878-021-00765-7
    DOI: 10.1007/s10878-021-00765-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-021-00765-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-021-00765-7?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. Friedman, Lea & Sinuany-Stern, Zilla, 1997. "Scaling units via the canonical correlation analysis in the DEA context," European Journal of Operational Research, Elsevier, vol. 100(3), pages 629-637, August.
    2. Li, Xiao-Bai & Reeves, Gary R., 1999. "A multiple criteria approach to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 507-517, June.
    3. Kirschstein, Thomas & Meisel, Frank, 2019. "A multi-period multi-commodity lot-sizing problem with supplier selection, storage selection and discounts for the process industry," European Journal of Operational Research, Elsevier, vol. 279(2), pages 393-406.
    4. Tone, Kaoru & Chang, Tsung-Sheng & Wu, Chen-Hui, 2020. "Handling negative data in slacks-based measure data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 282(3), pages 926-935.
    5. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    6. Chen, Lisa Y. & Wang, Tien-Chin, 2009. "Optimizing partners' choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR," International Journal of Production Economics, Elsevier, vol. 120(1), pages 233-242, July.
    7. Kannan, Devika & Jabbour, Ana Beatriz Lopes de Sousa & Jabbour, Charbel José Chiappetta, 2014. "Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company," European Journal of Operational Research, Elsevier, vol. 233(2), pages 432-447.
    8. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    9. M C A Silva Portela & E Thanassoulis & G Simpson, 2004. "Negative data in DEA: a directional distance approach applied to bank branches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1111-1121, October.
    10. Zarghami, Mahdi & Szidarovszky, Ferenc, 2009. "Revising the OWA operator for multi criteria decision making problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 198(1), pages 259-265, October.
    11. Sueyoshi, Toshiyuki, 1999. "DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives," Omega, Elsevier, vol. 27(3), pages 315-326, June.
    12. Timothy Anderson & Keith Hollingsworth & Lane Inman, 2002. "The Fixed Weighting Nature of A Cross-Evaluation Model," Journal of Productivity Analysis, Springer, vol. 17(3), pages 249-255, May.
    13. Rakesh D. Raut & Sachin S. Kamble & Manoj G. Kharat & Hemendu Joshi & Chirag Singhal & Sheetal J. Kamble, 2017. "A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 26(2), pages 203-223.
    14. Ho, William & Xu, Xiaowei & Dey, Prasanta K., 2010. "Multi-criteria decision making approaches for supplier evaluation and selection: A literature review," European Journal of Operational Research, Elsevier, vol. 202(1), pages 16-24, April.
    15. Cheng, Gang & Zervopoulos, Panagiotis & Qian, Zhenhua, 2013. "A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 225(1), pages 100-105.
    16. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    17. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2007. "Extended VIKOR method in comparison with outranking methods," European Journal of Operational Research, Elsevier, vol. 178(2), pages 514-529, April.
    18. Zon-Yau Lee & Chung-Che Pai, 2015. "Applying Improved DEA & VIKOR Methods to Evaluate the Operation Performance for World's Major TFT–LCD Manufacturers," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(03), pages 1-33.
    19. Asmild, Mette & Pastor, Jesús T., 2010. "Slack free MEA and RDM with comprehensive efficiency measures," Omega, Elsevier, vol. 38(6), pages 475-483, December.
    20. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    21. Adel HATAMI-MARBINI & Per J. AGRELL & Madjid TAVANA & Pegah KHOSHNEVIS, 2017. "A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing," LIDAM Reprints CORE 2880, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    22. Hashimoto, Akihiro, 1997. "A ranked voting system using a DEA/AR exclusion model: A note," European Journal of Operational Research, Elsevier, vol. 97(3), pages 600-604, March.
    23. Chunguang Bai & Simonov Kusi-Sarpong & Hadi Badri Ahmadi & Joseph Sarkis, 2019. "Social sustainable supplier evaluation and selection: a group decision-support approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7046-7067, November.
    24. Huchang Liao & Xiaomei Mi & Zeshui Xu, 2020. "A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 81-134, March.
    25. Florian Badorf & Stephan M. Wagner & Kai Hoberg & Felix Papier, 2019. "How Supplier Economies of Scale Drive Supplier Selection Decisions," Journal of Supply Chain Management, Institute for Supply Management, vol. 55(3), pages 45-67, July.
    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. Feifei Ye & Rongyan You & Haitian Lu & Sirui Han & Long-Hao Yang, 2023. "The Classification Impact of Different Types of Environmental Regulation on Chinese Provincial Carbon Emission Efficiency," Sustainability, MDPI, vol. 15(15), pages 1-24, August.
    2. Parisa Rafigh & Ali Akbar Akbari & Hadi Mohammadi Bidhandi & Ali Husseinzadeh Kashan, 2022. "A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models," Journal of Combinatorial Optimization, Springer, vol. 44(3), pages 1387-1432, October.
    3. Katerina Fotova Čiković & Ivana Martinčević & Joško Lozić, 2022. "Application of Data Envelopment Analysis (DEA) in the Selection of Sustainable Suppliers: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(11), pages 1-30, May.
    4. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, 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. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    2. Soltanifar, Mehdi & Shahghobadi, Saeid, 2013. "Selecting a benevolent secondary goal model in data envelopment analysis cross-efficiency evaluation by a voting model," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 65-74.
    3. Kang, Hee Jay & Kim, Changhee & Choi, Kanghwa, 2024. "Combining bootstrap data envelopment analysis with social networks for rank discrimination and suitable potential benchmarks," European Journal of Operational Research, Elsevier, vol. 312(1), pages 283-297.
    4. Lin, Shuguang & Shi, Hai-Liu & Wang, Ying-Ming, 2022. "An integrated slacks-based super-efficiency measure in the presence of nonpositive data," Omega, Elsevier, vol. 111(C).
    5. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    6. Lee, Hsuan-Shih, 2021. "Slacks-based measures of efficiency and super-efficiency in presence of nonpositive data," Omega, Elsevier, vol. 103(C).
    7. Tavana, Madjid & Izadikhah, Mohammad & Toloo, Mehdi & Roostaee, Razieh, 2021. "A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures," Omega, Elsevier, vol. 102(C).
    8. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 363-391, June.
    9. Aneirson Francisco Silva & Fernando Augusto S. Marins & Erica Ximenes Dias, 2020. "Improving the discrimination power with a new multi-criteria data envelopment model," Annals of Operations Research, Springer, vol. 287(1), pages 127-159, April.
    10. Lin, Ruiyue & Liu, Yue, 2019. "Super-efficiency based on the directional distance function in the presence of negative data," Omega, Elsevier, vol. 85(C), pages 26-34.
    11. Bao Jiang & Wenxue Feng & Jian Li, 2022. "Uncertain random data envelopment analysis for technical efficiency," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 1-20, March.
    12. Lin, Ruiyue & Liu, Qian, 2021. "Multiplier dynamic data envelopment analysis based on directional distance function: An application to mutual funds," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1043-1057.
    13. Ghasemi, M.-R. & Ignatius, Joshua & Emrouznejad, Ali, 2014. "A bi-objective weighted model for improving the discrimination power in MCDEA," European Journal of Operational Research, Elsevier, vol. 233(3), pages 640-650.
    14. Mohammad Izadikhah & Reza Farzipoor Saen & Razieh Roostaee, 2018. "How to assess sustainability of suppliers in the presence of volume discount and negative data in data envelopment analysis?," Annals of Operations Research, Springer, vol. 269(1), pages 241-267, October.
    15. Tone, Kaoru & Chang, Tsung-Sheng & Wu, Chen-Hui, 2020. "Handling negative data in slacks-based measure data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 282(3), pages 926-935.
    16. Rezaeiani, M.J. & Foroughi, A.A., 2018. "Ranking efficient decision making units in data envelopment analysis based on reference frontier share," European Journal of Operational Research, Elsevier, vol. 264(2), pages 665-674.
    17. Fang, Tao & Fang, Debin & Yu, Bolin, 2022. "Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants," Energy Policy, Elsevier, vol. 165(C).
    18. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    19. Yadav, Vinod Kumar & Padhy, N.P. & Gupta, H.O., 2011. "Performance evaluation and improvement directions for an Indian electric utility," Energy Policy, Elsevier, vol. 39(11), pages 7112-7120.
    20. Volkan Soner Özsoy & Mediha Örkcü & H. Hasan Örkcü, 2021. "A minimax approach for selecting the overall and stage-level most efficient unit in two stage production processes," Annals of Operations Research, Springer, vol. 300(1), pages 137-169, May.

    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:spr:jcomop:v:43:y:2022:i:1:d:10.1007_s10878-021-00765-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.