IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0245187.html
   My bibliography  Save this article

A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation

Author

Listed:
  • Do Anh Duc
  • Luu Huu Van
  • Vincent F Yu
  • Shuo-Yan Chou
  • Ngo Van Hien
  • Ngo The Chi
  • Dinh Van Toan
  • Luu Quoc Dat

Abstract

Supplier selection and segmentation are crucial tasks of companies in order to reduce costs and increase the competitiveness of their goods. To handle uncertainty and dynamicity in the supplier segmentation problem, this research thus proposes a new dynamic generalized fuzzy multi-criteria group decision making (MCGDM) approach from the aspects of capability and willingness and with respect to environmental issues. The proposed approach defines the aggregated ratings of alternatives, the aggregated weights of criteria, and the weighted ratings by using generalized fuzzy numbers with the effect of time weight. Next, we determine the ranking order of alternatives via a popular centroid-index ranking approach. Finally, two case studies demonstrate the efficiency of the proposed dynamic approach. The applications show that the proposed appoach is effective in solving the MCGDM in vague environment.

Suggested Citation

  • Do Anh Duc & Luu Huu Van & Vincent F Yu & Shuo-Yan Chou & Ngo Van Hien & Ngo The Chi & Dinh Van Toan & Luu Quoc Dat, 2021. "A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-13, January.
  • Handle: RePEc:plo:pone00:0245187
    DOI: 10.1371/journal.pone.0245187
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245187
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0245187&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0245187?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
    ---><---

    References listed on IDEAS

    as
    1. Hosseini, Seyedmohsen & Morshedlou, Nazanin & Ivanov, Dmitry & Sarder, M.D. & Barker, Kash & Khaled, Abdullah Al, 2019. "Resilient supplier selection and optimal order allocation under disruption risks," International Journal of Production Economics, Elsevier, vol. 213(C), pages 124-137.
    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. Fakhradin Ghasemi & Mohammad Babamiri & Zahra Pashootan, 2022. "A comprehensive method for the quantification of medication error probability based on fuzzy SLIM," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-17, February.
    2. Thalles Vitelli Garcez & Helder Tenório Cavalcanti & Adiel Teixeira de Almeida, 2021. "A hybrid decision support model using Grey Relational Analysis and the Additive-Veto Model for solving multicriteria decision-making problems: an approach to supplier selection," Annals of Operations Research, Springer, vol. 304(1), pages 199-231, September.

    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. Maciej Urbaniak & Piotr Rogala & Piotr Kafel, 2023. "Expectations of manufacturing companies regarding future priorities of improvement actions taken by their suppliers," Operations Management Research, Springer, vol. 16(1), pages 296-310, March.
    2. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    3. Shraddha Mishra & Surya Prakash Singh, 2022. "Designing dynamic reverse logistics network for post-sale service," Annals of Operations Research, Springer, vol. 310(1), pages 89-118, March.
    4. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    5. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    6. Garvey, Myles D. & Carnovale, Steven, 2020. "The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation," International Journal of Production Economics, Elsevier, vol. 228(C).
    7. Huang, Xiaobing & Xie, Jiawei, 2024. "Impact of green credit policies on the global value chain position of heavy polluting enterprises," Energy Policy, Elsevier, vol. 192(C).
    8. Belleh Fontem & Megan Price, 2021. "Joint client selection and contract design for a risk-averse commodity broker in a two-echelon supply chain," Annals of Operations Research, Springer, vol. 307(1), pages 111-138, December.
    9. Manu Sharma & Sudhanshu Joshi & Sunil Luthra & Anil Kumar, 2022. "Managing disruptions and risks amidst COVID-19 outbreaks: role of blockchain technology in developing resilient food supply chains," Operations Management Research, Springer, vol. 15(1), pages 268-281, June.
    10. Li, Yuhong & Zobel, Christopher W. & Seref, Onur & Chatfield, Dean, 2020. "Network characteristics and supply chain resilience under conditions of risk propagation," International Journal of Production Economics, Elsevier, vol. 223(C).
    11. Rayenda K. Brahmana & Hui‐Wei You & Evan Lau, 2022. "Does reputation matter for firm risk in developing country?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2110-2123, April.
    12. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    13. Alptekin Ulutaş & Mladen Krstić & Ayşe Topal & Leonardo Agnusdei & Snežana Tadić & Pier Paolo Miglietta, 2024. "A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem," Mathematics, MDPI, vol. 12(10), pages 1-22, May.
    14. Mosayebi, Mohsen & Fathi, Michel & Hedayati, Mehrnaz Khalaj & Ivanov, Dmitry, 2024. "Time-to-Adapt (TTA)," International Journal of Production Economics, Elsevier, vol. 278(C).
    15. Nocera, Fabrizio & Contento, Alessandro & Gardoni, Paolo, 2024. "Risk analysis of supply chains: The role of supporting structures and infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    16. Hu, Shaolong & Dong, Zhijie Sasha & Lev, Benjamin, 2022. "Supplier selection in disaster operations management: Review and research gap identification," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    17. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    18. Liu, Ming & Liu, Zhongzheng & Chu, Feng & Dolgui, Alexandre & Chu, Chengbin & Zheng, Feifeng, 2022. "An optimization approach for multi-echelon supply chain viability with disruption risk minimization," Omega, Elsevier, vol. 112(C).
    19. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    20. Li, Siping & Zhou, Yaoming, 2024. "Integrating equity and efficiency into urban logistics resilience under emergency lockdowns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

    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:plo:pone00:0245187. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.