IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v119y2016icp1-17.html
   My bibliography  Save this article

An integrated data envelopment analysis and simulation method for group consensus ranking

Author

Listed:
  • Ebrahimnejad, Ali
  • Tavana, Madjid
  • Santos-Arteaga, Francisco J.

Abstract

Group consensus ranking is an important topic in performance evaluation and selection research. Data envelopment analysis (DEA) has been used for obtaining an efficiency score (preference score) for each candidate. We propose an integrated DEA and simulation method for group consensus ranking. The ranking method proposed in this study has several unique features. In contrast to most voting methods that assume equal voting power to voters, the proposed method classifies voters into different groups and allows for assigning a different voting power to each group. In spite of its effectiveness, though similarly to the competing methods in the literature, the proposed method may lead to more than one efficient candidate. Several ranking models are extended and used to discriminate among the efficient candidates. Despite the wealth of information provided to the decision maker(s), different extended ranking models may produce different rankings. Simulation is used to analyze these rankings and synthesize them into one overall group ranking. A case study is used to demonstrate the applicability and exhibit the efficacy of the proposed method.

Suggested Citation

  • Ebrahimnejad, Ali & Tavana, Madjid & Santos-Arteaga, Francisco J., 2016. "An integrated data envelopment analysis and simulation method for group consensus ranking," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 1-17.
    • Handle: RePEc:eee:matcom:v:119:y:2016:i:c:p:1-17
    DOI: 10.1016/j.matcom.2015.08.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475415001871
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2015.08.022?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. 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.
    2. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.
    3. Foroughi, A.A. & Tamiz, M., 2005. "An effective total ranking model for a ranked voting system," Omega, Elsevier, vol. 33(6), pages 491-496, December.
    4. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    5. Wolinsky, Asher, 2002. "Eliciting information from multiple experts," Games and Economic Behavior, Elsevier, vol. 41(1), pages 141-160, October.
    6. Green, Rodney H. & Doyle, John R. & Cook, Wade D., 1996. "Preference voting and project ranking using DEA and cross-evaluation," European Journal of Operational Research, Elsevier, vol. 90(3), pages 461-472, May.
    7. Wade D. Cook & Moshe Kress, 1990. "A Data Envelopment Model for Aggregating Preference Rankings," Management Science, INFORMS, vol. 36(11), pages 1302-1310, November.
    8. Llamazares, Bonifacio & Pea, Teresa, 2009. "Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates," European Journal of Operational Research, Elsevier, vol. 197(2), pages 714-721, September.
    9. Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, December.
    10. Brams, Steven J. & Fishburn, Peter C., 2002. "Voting procedures," Handbook of Social Choice and Welfare, in: K. J. Arrow & A. K. Sen & K. Suzumura (ed.), Handbook of Social Choice and Welfare, edition 1, volume 1, chapter 4, pages 173-236, Elsevier.
    11. Ramilan, Thiagarajah & Scrimgeour, Frank & Marsh, Dan, 2011. "Analysis of environmental and economic efficiency using a farm population micro-simulation model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1344-1352.
    12. Obata, Tsuneshi & Ishii, Hiroaki, 2003. "A method for discriminating efficient candidates with ranked voting data," European Journal of Operational Research, Elsevier, vol. 151(1), pages 233-237, November.
    13. Liang, Liang & Wu, Jie & Cook, Wade D. & Zhu, Joe, 2008. "Alternative secondary goals in DEA cross-efficiency evaluation," International Journal of Production Economics, Elsevier, vol. 113(2), pages 1025-1030, June.
    14. Robert Dur & Otto H. Swank, 2005. "Producing and Manipulating Information," Economic Journal, Royal Economic Society, vol. 115(500), pages 185-199, January.
    15. TAVANA, Madjid & HATAMI-MARBINI, Adel, 2011. "A group AHP-TOPSIS framework for human spaceflight mission planning at NASA," LIDAM Reprints CORE 2362, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. 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.
    17. Lin, Rung-Chuan & Sir, Mustafa Y. & Pasupathy, Kalyan S., 2013. "Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services," Omega, Elsevier, vol. 41(5), pages 881-892.
    18. SHOKOUHI, Amir H. & HATAMI-MARBINI, Adel & TAVANA, Madjid & SAATI, Saber, 2010. "A robust optimization approach for imprecise data envelopment analysis," LIDAM Reprints CORE 2215, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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. Li-Ching Ma, 2018. "Discovering Consensus Preferences Visually Based on Gower Plots," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 741-761, May.
    2. Georgios Tsaples & Jason Papathanasiou & Andreas C. Georgiou, 2022. "An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    3. Chaofan Xian & Shuo Yang & Yupeng Fan & Haotong Wu & Cheng Gong, 2022. "Coupling Efficiency Assessment of Food–Energy–Water (FEW) Nexus Based on Urban Resource Consumption towards Economic Development: The Case of Shenzhen Megacity, China," Land, MDPI, vol. 11(10), pages 1-25, October.
    4. Kao, Chiang & Liu, Shiang-Tai, 2022. "Group decision making in data envelopment analysis: A robot selection application," European Journal of Operational Research, Elsevier, vol. 297(2), pages 592-599.
    5. Mehdi Toloo & Madjid Tavana & Francisco J. Santos-Arteaga, 2019. "An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights," 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. 27(4), pages 887-904, December.
    6. K. Kounetas & G. Androulakis & M. Kaisari & G. Manousakis, 2023. "Educational reforms and secondary school's efficiency performance in Greece: a bootstrap DEA and multilevel approach," Operational Research, Springer, vol. 23(1), pages 1-29, March.
    7. Mohammad Izadikhah & Reza Farzipoor Saen, 2019. "Solving voting system by data envelopment analysis for assessing sustainability of suppliers," Group Decision and Negotiation, Springer, vol. 28(3), pages 641-669, June.

    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. 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.
    2. Paolo Viappiani, 2024. "Volumetric Aggregation Methods for Scoring Rules with Unknown Weights," Post-Print hal-04440153, HAL.
    3. Madjid Tavana & Mehdi Soltanifar & Francisco J. Santos-Arteaga, 2023. "Analytical hierarchy process: revolution and evolution," Annals of Operations Research, Springer, vol. 326(2), pages 879-907, July.
    4. Llamazares, Bonifacio & Peña, Teresa, 2013. "Aggregating preferences rankings with variable weights," European Journal of Operational Research, Elsevier, vol. 230(2), pages 348-355.
    5. Bonifacio Llamazares, 2016. "Ranking Candidates Through Convex Sequences of Variable Weights," Group Decision and Negotiation, Springer, vol. 25(3), pages 567-584, May.
    6. Llamazares, Bonifacio & Pea, Teresa, 2009. "Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates," European Journal of Operational Research, Elsevier, vol. 197(2), pages 714-721, September.
    7. Llamazares, Bonifacio, 2024. "Ranking voting systems and surrogate weights: Explicit formulas for centroid weights," European Journal of Operational Research, Elsevier, vol. 317(3), pages 967-976.
    8. Mohammad Izadikhah & Reza Farzipoor Saen, 2019. "Solving voting system by data envelopment analysis for assessing sustainability of suppliers," Group Decision and Negotiation, Springer, vol. 28(3), pages 641-669, June.
    9. Y M Wang & K S Chin & J B Yang, 2007. "Three new models for preference voting and aggregation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1389-1393, October.
    10. Paolo Viappiani, 2020. "Robust winner determination in positional scoring rules with uncertain weights," Theory and Decision, Springer, vol. 88(3), pages 323-367, April.
    11. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    12. 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.
    13. Obata, Tsuneshi & Ishii, Hiroaki, 2003. "A method for discriminating efficient candidates with ranked voting data," European Journal of Operational Research, Elsevier, vol. 151(1), pages 233-237, November.
    14. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2016. "Revisiting the standing of international business journals in the competitive landscape," Journal of World Business, Elsevier, vol. 51(4), pages 487-498.
    15. Foroughi, A.A. & Tamiz, M., 2005. "An effective total ranking model for a ranked voting system," Omega, Elsevier, vol. 33(6), pages 491-496, December.
    16. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.
    17. Bonifacio Llamazares & Teresa Peña, 2015. "Positional Voting Systems Generated by Cumulative Standings Functions," Group Decision and Negotiation, Springer, vol. 24(5), pages 777-801, September.
    18. Jie Wu & Junfei Chu & Qingyuan Zhu & Pengzhen Yin & Liang Liang, 2016. "DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 5990-6007, October.
    19. 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.
    20. Ignacio Contreras, 2010. "A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights," Group Decision and Negotiation, Springer, vol. 19(5), pages 441-456, September.

    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:matcom:v:119:y:2016:i:c:p:1-17. 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.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.