IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v18y2018i3d10.1007_s12351-017-0344-3.html
   My bibliography  Save this article

Developing a rough set based approach for group decision making based on determining weights of decision makers with interval numbers

Author

Listed:
  • Qiang Yang

    (Xihua University)

  • Ping-an Du

    (University of Electronic Science and Technology of China)

  • Yong Wang

    (Southwest China Institute of Electronic Technology)

  • Bin Liang

    (Southwest China Institute of Electronic Technology)

Abstract

The goal of this paper is to propose a novel approach for determining the weights of decision makers (DMs) in group settings with a rough set group method, in which each decision maker’s decision matrix is in interval numbers. In this paper, we first build a lower rough group decision (LRGD) and an upper rough group decision (URGD) from a rough group decision. Then, we define the average matrix of LRGD as a Lower positive ideal solution (Lower PIS), and the average matrix of URGD as an Upper positive ideal solution (Upper PIS) based on the Technique for Order Preference by Similarity to Ideal Solution method. Next, the average matrix of the Lower PIS and Upper PIS is regarded as the positive ideal solution (PIS), and the farthest distance from the PIS is regarded as the negative ideal solution (NIS). After that, each DM’s weight is derived from the distances from the DM’s decision to the PIS and NIS. Comparisons with existing methods are also made. Finally, an example of air quality evaluation is provided to clarify the availability of the proposed method.

Suggested Citation

  • Qiang Yang & Ping-an Du & Yong Wang & Bin Liang, 2018. "Developing a rough set based approach for group decision making based on determining weights of decision makers with interval numbers," Operational Research, Springer, vol. 18(3), pages 757-779, October.
  • Handle: RePEc:spr:operea:v:18:y:2018:i:3:d:10.1007_s12351-017-0344-3
    DOI: 10.1007/s12351-017-0344-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-017-0344-3
    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/s12351-017-0344-3?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. Kumar, Ravi & Singal, S.K., 2015. "Penstock material selection in small hydropower plants using MADM methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 240-255.
    2. Kusi-Sarpong, Simonov & Bai, Chunguang & Sarkis, Joseph & Wang, Xuping, 2015. "Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology," Resources Policy, Elsevier, vol. 46(P1), pages 86-100.
    3. R.C. Van den Honert, 2001. "Decisional Power in Group Decision Making: A Note on the Allocation of Group Members' Weights in the Multiplicative AHP and SMART," Group Decision and Negotiation, Springer, vol. 10(3), pages 275-286, May.
    4. Pawlak, Zdzisaw & Sowinski, Roman, 1994. "Rough set approach to multi-attribute decision analysis," European Journal of Operational Research, Elsevier, vol. 72(3), pages 443-459, February.
    5. Kim, Soung Hie & Choi, Sang Hyun & Kim, Jae Kyeong, 1999. "An interactive procedure for multiple attribute group decision making with incomplete information: Range-based approach," European Journal of Operational Research, Elsevier, vol. 118(1), pages 139-152, October.
    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. Qiang Yang & Ping-an Du & Yong Wang & Bin Liang, 2017. "A rough set approach for determining weights of decision makers in group decision making," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    2. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.
    3. Dias, Luis C. & Climaco, Joao N., 2005. "Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture," European Journal of Operational Research, Elsevier, vol. 160(2), pages 291-307, January.
    4. Azam, Nouman & Zhang, Yan & Yao, JingTao, 2017. "Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets," European Journal of Operational Research, Elsevier, vol. 261(2), pages 704-714.
    5. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    6. Hu, Qiwei & Chakhar, Salem & Siraj, Sajid & Labib, Ashraf, 2017. "Spare parts classification in industrial manufacturing using the dominance-based rough set approach," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1136-1163.
    7. Salvatore Barbagallo & Simona Consoli & Nello Pappalardo & Salvatore Greco & Santo Zimbone, 2006. "Discovering Reservoir Operating Rules by a Rough Set Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 19-36, February.
    8. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    9. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    10. Hahn, Eugene D., 2006. "Link function selection in stochastic multicriteria decision making models," European Journal of Operational Research, Elsevier, vol. 172(1), pages 86-100, July.
    11. Fei-Hsin Huang & Hann Nguyen, 2022. "Selecting Optimal Cultural Tourism for Indigenous Tribes by Fuzzy MCDM," Mathematics, MDPI, vol. 10(17), pages 1-12, August.
    12. Xiaoyang Zhou & Yan Tu & Jing Han & Jiuping Xu & Xionghui Ye, 2017. "A Class of Level-2 Fuzzy Decision-Making Model with Expected Objectives and Chance Constraints: Application to Supply Chain Network Design," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 907-938, July.
    13. Zhang, Hengjie & Dong, Yucheng & Chiclana, Francisco & Yu, Shui, 2019. "Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design," European Journal of Operational Research, Elsevier, vol. 275(2), pages 580-598.
    14. Hsu-Shih Shih, 2016. "A Mixed-Data Evaluation in Group TOPSIS with Differentiated Decision Power," Group Decision and Negotiation, Springer, vol. 25(3), pages 537-565, May.
    15. Soleimani, Hamed, 2021. "A new sustainable closed-loop supply chain model for mining industry considering fixed-charged transportation: A case study in a travertine quarry," Resources Policy, Elsevier, vol. 74(C).
    16. Hocine, Amine & Kouaissah, Noureddine, 2020. "XOR analytic hierarchy process and its application in the renewable energy sector," Omega, Elsevier, vol. 97(C).
    17. Yamoah, Fred A. & Sivarajah, Uthayasankar & Mahroof, Kamran & Peña, Iker González, 2022. "Demystifying corporate inertia towards transition to circular economy: A management frame of reference," International Journal of Production Economics, Elsevier, vol. 244(C).
    18. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    19. Yun Kang & Shunxiang Wu & Yuwen Li & Wei Weng, 2017. "New and improved: grey multi-granulation rough sets," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(12), pages 2575-2589, September.
    20. Gülgönül Bozoğlu Batı & İsmail Hakkı Armutlulu, 2020. "Work and family conflict analysis of female entrepreneurs in Turkey and classification with rough set theory," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-12, December.

    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:operea:v:18:y:2018:i:3:d:10.1007_s12351-017-0344-3. 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.