Comparative analysis of novel fuzzy multi-criteria decision-making methods for selecting fourth-party logistics service providers: a case study in the plastic resin industry
Issued Date
2023-12-02
Resource Type
ISSN
17558077
eISSN
17558085
Scopus ID
2-s2.0-85179135591
Journal Title
International Journal of Applied Decision Sciences
Volume
17
Issue
1
Start Page
88
End Page
119
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Applied Decision Sciences Vol.17 No.1 (2023) , 88-119
Suggested Citation
Sumrit D., Jiamanukulkij K. Comparative analysis of novel fuzzy multi-criteria decision-making methods for selecting fourth-party logistics service providers: a case study in the plastic resin industry. International Journal of Applied Decision Sciences Vol.17 No.1 (2023) , 88-119. 119. doi:10.1504/IJADS.2024.135201 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/91499
Title
Comparative analysis of novel fuzzy multi-criteria decision-making methods for selecting fourth-party logistics service providers: a case study in the plastic resin industry
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
This paper proposes a systematic framework to select the best 4PLs by incorporating several MCDM methods. The aim of this paper is to conduct a comparative study to examine how different MCDM methods compare when apply for 4PLs selecting problem. First, 14 criteria of 4PLs selection are identified through literature and input from industrial experts. Second, the objective weights of criteria are derived through interval Shannon’s entropy based on α-level sets. Afterward, the 4PL candidates are ranked comparatively using five novel MCDM methods reported in literature including CoCoSo, MARCOS, EDAS, MAIRCA, and CODAS. Finally, the sensitivity analysis is performed to test the robustness and reliability of the proposed framework. A case of the plastic resin industry in Thailand is used to demonstrate the application of the proposed framework. The practitioners and academics can utilise the proposed framework to select the best 4PLs.