IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4309544.html
   My bibliography  Save this article

A New TOPSIS Approach Using Cosine Similarity Measures and Cubic Bipolar Fuzzy Information for Sustainable Plastic Recycling Process

Author

Listed:
  • Muhammad Riaz
  • Dragan Pamucar
  • Anam Habib
  • Mishal Riaz

Abstract

A cubic bipolar fuzzy set (CBFS) is a robust paradigm to express bipolarity and vagueness in terms of bipolar fuzzy numbers and interval-valued bipolar fuzzy numbers. The abstraction of similarity measures (SMs) has a large number of applications in various fields. Therefore, in this study, taking the advantage of CBFSs, three cosine similarity measures for CBFSs are proposed successively by using cosine of the angle between two vectors, new distance measures, and cosine function. Some key properties of these similarity measures (SMs) are explored. Based on suggested SMs, the problem of bacteria recognition is analyzed and an important application is provided to exhibit the efficiency of proposed SMs for CBF information. Moreover, the TOPSIS approach based on cosine SMs is developed for multicriteria group decision-making (MCGDM) problems. An illustrative example about the selection of sustainable plastic recycling process is presented to discuss the efficiency of the suggested MCGDM technique.

Suggested Citation

  • Muhammad Riaz & Dragan Pamucar & Anam Habib & Mishal Riaz, 2021. "A New TOPSIS Approach Using Cosine Similarity Measures and Cubic Bipolar Fuzzy Information for Sustainable Plastic Recycling Process," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, December.
    • Handle: RePEc:hin:jnlmpe:4309544
    DOI: 10.1155/2021/4309544
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/4309544.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/4309544.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/4309544?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Iftikhar Ul Haq & Tanzeela Shaheen & Wajid Ali & Hamza Toor & Tapan Senapati & Francesco Pilla & Sarbast Moslem, 2023. "Novel Fermatean Fuzzy Aczel–Alsina Model for Investment Strategy Selection," Mathematics, MDPI, vol. 11(14), pages 1-23, July.

    More about this item

    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:hin:jnlmpe:4309544. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.