IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v322y2025i2p615-628.html
   My bibliography  Save this article

Optimal ranking model of fuzzy preference relations with self-confidence for addressing self-confidence failure

Author

Listed:
  • Zhang, Chonghui
  • Luo, Dandan
  • Su, Weihua
  • Benjamin, Lev

Abstract

Fuzzy preference relation with self-confidence (FPR-SC) uses semantic self-confidence to illustrate the hesitation of experts in affirming given preference values. However, extant ranking derivation methods of FPRs-SC suffer from self-confidence failure problem. Specifically, when the logical operations of self-confidence levels are replaced by algebraic operations on semantic subscripts, the derived rankings may be unstable or independent of the self-confidence. To address this issue, an optimal ranking model of FPR-SC with chance-constrained is proposed and extended to group decision-making. Based on the concept of ‘reliability level’ in parameter estimation, the evaluation information expressed by ‘preference values + self-confidence levels’ is first explained using probability distributions to achieve dimensional unity between qualitative self-confidence and quantitative preference. A multiplicative consistency-driven optimal model is then designed to assess the individuality of self-confidence. Guided by the ‘3σ’ principle, FPR-SC is further replaced by random variables following asymmetric bilateral truncated normal distributions. This transformation captures the inner cognition of individuals during subjective judgment, and ensures effective constraints on the numerical range through the asymmetric design. Finally, motivated by the minimization of information deviation, an FPR-SC optimal ranking model with chance-constrained is constructed, and its effectiveness is verified.

Suggested Citation

  • Zhang, Chonghui & Luo, Dandan & Su, Weihua & Benjamin, Lev, 2025. "Optimal ranking model of fuzzy preference relations with self-confidence for addressing self-confidence failure," European Journal of Operational Research, Elsevier, vol. 322(2), pages 615-628.
    • Handle: RePEc:eee:ejores:v:322:y:2025:i:2:p:615-628
    DOI: 10.1016/j.ejor.2024.11.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724008580
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2024.11.011?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. Xunjie Gou & Zeshui Xu & Xinxin Wang & Huchang Liao, 2021. "Managing consensus reaching process with self-confident double hierarchy linguistic preference relations in group decision making," Fuzzy Optimization and Decision Making, Springer, vol. 20(1), pages 51-79, March.
    2. Biljana N. Adebambo & Xuemin (Sterling) Yan, 2018. "Investor Overconfidence, Firm Valuation, and Corporate Decisions," Management Science, INFORMS, vol. 64(11), pages 5349-5369, November.
    3. Zhi Chen & Daniel Kuhn & Wolfram Wiesemann, 2024. "Technical Note—Data-Driven Chance Constrained Programs over Wasserstein Balls," Operations Research, INFORMS, vol. 72(1), pages 410-424, January.
    4. Zhen Zhang & Xinyue Kou & Wenyu Yu & Yuan Gao, 2021. "Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(8), pages 1914-1927, August.
    5. Maxime C. Cohen & Philipp W. Keller & Vahab Mirrokni & Morteza Zadimoghaddam, 2019. "Overcommitment in Cloud Services: Bin Packing with Chance Constraints," Management Science, INFORMS, vol. 65(7), pages 3255-3271, July.
    6. Wenqi Liu & Yucheng Dong & Francisco Chiclana & Francisco Javier Cabrerizo & Enrique Herrera-Viedma, 2017. "Group decision-making based on heterogeneous preference relations with self-confidence," Fuzzy Optimization and Decision Making, Springer, vol. 16(4), pages 429-447, December.
    7. Zaiwu Gong & Xiao Tan & Yingjie Yang, 2019. "Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1296-1307, August.
    8. Miguel A. Lejeune & François Margot, 2016. "Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities," Operations Research, INFORMS, vol. 64(4), pages 939-957, August.
    9. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    10. Wenqi Liu & Hengjie Zhang & Haiming Liang & Cong-cong Li & Yucheng Dong, 2022. "Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method," Group Decision and Negotiation, Springer, vol. 31(1), pages 213-240, February.
    Full references (including those not matched with items on IDEAS)

    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. Xiaodi Bai & Jie Sun & Xiaojin Zheng, 2021. "An Augmented Lagrangian Decomposition Method for Chance-Constrained Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1056-1069, July.
    2. Wang, Shanshan & Mehrotra, Sanjay & Peng, Chun, 2025. "Robust concave utility maximization over chance constraints," European Journal of Operational Research, Elsevier, vol. 321(3), pages 800-813.
    3. Gong, Zaiwu & Guo, Weiwei & Herrera-Viedma, Enrique & Gong, Zejun & Wei, Guo, 2020. "Consistency and consensus modeling of linear uncertain preference relations," European Journal of Operational Research, Elsevier, vol. 283(1), pages 290-307.
    4. Anand Deo & Karthyek Murthy, 2025. "The Scaling Behaviors in Achieving High Reliability via Chance-Constrained Optimization," Papers 2504.07728, arXiv.org.
    5. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    6. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    7. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    8. Ghazale Kordi & Parsa Hasanzadeh-Moghimi & Mohammad Mahdi Paydar & Ebrahim Asadi-Gangraj, 2023. "A multi-objective location-routing model for dental waste considering environmental factors," Annals of Operations Research, Springer, vol. 328(1), pages 755-792, September.
    9. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    10. Jana, R.K. & Sharma, Dinesh K. & Chakraborty, B., 2016. "A hybrid probabilistic fuzzy goal programming approach for agricultural decision-making," International Journal of Production Economics, Elsevier, vol. 173(C), pages 134-141.
    11. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    12. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    13. Ümit Sakallı & Ömer Baykoç & Burak Birgören, 2011. "Stochastic optimization for blending problem in brass casting industry," Annals of Operations Research, Springer, vol. 186(1), pages 141-157, June.
    14. Hongyue Diao & Ansheng Deng & Hui Cui & Xin Liu & Li Zou, 2022. "An approach for solving fuzzy multi-criteria decision problem under linguistic information," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 45-69, March.
    15. Walid Ben-Ameur & Adam Ouorou & Guanglei Wang & Mateusz Żotkiewicz, 2018. "Multipolar robust optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 395-434, December.
    16. G. Pantuso & L. M. Hvattum, 2021. "Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 583-611, July.
    17. Torkamani, Javad, 2005. "Using a whole-farm modelling approach to assess prospective technologies under uncertainty," Agricultural Systems, Elsevier, vol. 85(2), pages 138-154, August.
    18. Yanikoglu, I. & den Hertog, D., 2011. "Safe Approximations of Chance Constraints Using Historical Data," Other publications TiSEM ab77f6f2-248a-42f1-bde1-0, Tilburg University, School of Economics and Management.
    19. Şeyda Gür & Mehmet Pınarbaşı & Hacı Mehmet Alakaş & Tamer Eren, 2023. "Operating room scheduling with surgical team: a new approach with constraint programming and goal programming," 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(4), pages 1061-1085, December.
    20. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.

    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:322:y:2025:i:2:p:615-628. 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.