IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2016i4p205-215.html
   My bibliography  Save this article

Multicriteria Decision-Making Method Based On Cosine Similarity Measures Between Interval- Valued Fuzzy Sets With Risk Preference

Author

Listed:
  • Jun YE

    (Department of Electrical and Information Engineering, Shaoxing University)

Abstract

This paper presents the cosine similarity measure between IVFSs with risk preference and gives its decision making method using the cosine similarity measure depending on decision makers’ optimistic, neutral, and pessimistic natures for the subjective judgments that accompany the decision making process. Through the weighted cosine similarity measure between an alternative and the ideal alternative corresponding to one of optimistic, neutral, and pessimistic choices desired by decision makers, we can determine the ranking order of alternatives and the best one. This choosing feature corresponding to decision makers’ preference makes the proposed method not only more flexible, but also more suitable for many practical applications. Finally, an illustrative example is presented to demonstrate the feasibility and applicability of the proposed method.

Suggested Citation

  • Jun YE, 2016. "Multicriteria Decision-Making Method Based On Cosine Similarity Measures Between Interval- Valued Fuzzy Sets With Risk Preference," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 205-215.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:4:p:205-215
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb4_2016p205-215.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Jun Ye, 2012. "Multicriteria Group Decision-Making Method Using Vector Similarity Measures For Trapezoidal Intuitionistic Fuzzy Numbers," Group Decision and Negotiation, Springer, vol. 21(4), pages 519-530, July.
    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. Yongming Song & Jun Hu, 2017. "Vector similarity measures of hesitant fuzzy linguistic term sets and their applications," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    2. Vuciterna, Rina & Thomsen, Michael & Popp, Jennie & Musliu, Arben, 2017. "Efficiency and Competitiveness of Kosovo Raspberry Producers," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252770, Southern Agricultural Economics Association.
    3. Carla Oliveira Henriques & Dulce Helena Coelho & Maria Elisabete Duarte Neves, 2022. "Investment planning in energy efficiency programs: a portfolio based approach," Operational Research, Springer, vol. 22(1), pages 615-649, March.
    4. Shubham Gupta & Raghav Khanna & Pranay Kohli & Sarthak Agnihotri & Umang Soni & M. Asjad, 2023. "Risk evaluation of electric vehicle charging infrastructure using Fuzzy AHP – a case study in India," Operations Management Research, Springer, vol. 16(1), pages 245-258, March.
    5. Berna Tektas Sivrikaya & Ferhan Cebi & Hasan Hüseyin Turan & Nihat Kasap & Dursun Delen, 2017. "A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts," Information Systems Frontiers, Springer, vol. 19(5), pages 975-991, October.
    6. Collan, Mikael, 2008. "New Method for Real Option Valuation Using Fuzzy Numbers," Working Papers 466, IAMSR, Åbo Akademi.
    7. Kim, Jong Soon & Whang, Kyu-Seung, 1998. "A tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function," European Journal of Operational Research, Elsevier, vol. 107(3), pages 614-624, June.
    8. Berna Tektaş & Hasan Hüseyin Turan & Nihat Kasap & Ferhan Çebi & Dursun Delen, 2022. "A Fuzzy Prescriptive Analytics Approach to Power Generation Capacity Planning," Energies, MDPI, vol. 15(9), pages 1-26, April.
    9. 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.
    10. Víctor G. Alfaro-García & Anna M. Gil-Lafuente & Gerardo G. Alfaro Calderón, 2017. "A fuzzy approach to a municipality grouping model towards creation of synergies," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 391-408, September.
    11. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    12. Wenyao Niu & Yuan Rong & Liying Yu & Lu Huang, 2022. "A Novel Hybrid Group Decision Making Approach Based on EDAS and Regret Theory under a Fermatean Cubic Fuzzy Environment," Mathematics, MDPI, vol. 10(17), pages 1-30, August.
    13. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    14. Baiyee-Mbi, Agbor-Baiyee & Mazzocco, Michael A., 2005. "Comparative Evaluation of the Performance of Spans of Control Designs in Grain Supply Chains," 2005 Annual meeting, July 24-27, Providence, RI 19313, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    15. Mikhailov, L., 2004. "A fuzzy approach to deriving priorities from interval pairwise comparison judgements," European Journal of Operational Research, Elsevier, vol. 159(3), pages 687-704, December.
    16. Hongyi Sun & Bingqian Zhang & Wenbin Ni, 2022. "A Hybrid Model Based on SEM and Fuzzy TOPSIS for Supplier Selection," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    17. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    18. Dinulescu Ruxandra & Dobrin Cosmin, 2022. "Applying the fuzzy analytical hierarchy process for classifying and prioritizing healthcare quality attributes," Management & Marketing, Sciendo, vol. 17(1), pages 15-40, March.
    19. Foulds, Les R. & do Nascimento, Hugo A.D. & Calixto, Iacer C.A.C. & Hall, Bryon R. & Longo, Humberto, 2013. "A fuzzy set-based approach to origin–destination matrix estimation in urban traffic networks with imprecise data," European Journal of Operational Research, Elsevier, vol. 231(1), pages 190-201.
    20. Markus Philipp Roessler & Joachim Metternich & Eberhard Abele, 2014. "Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability," Business and Management Research, Business and Management Research, Sciedu Press, vol. 3(2), pages 93-109, June.

    More about this item

    Keywords

    interval-valued fuzzy set; cosine similarity measure; multicriteria decision making;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    Statistics

    Access and download statistics

    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:cys:ecocyb:v:50:y:2016:i:4:p:205-215. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    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.