A Fuzzy-Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production

被引:13
作者
Chen, Xuemei [1 ]
Zhou, Bin [1 ]
Stilic, Andelka [2 ]
Stevic, Zeljko [3 ]
Puska, Adis [4 ]
机构
[1] Xian Univ Sci & Technol, Inst Marxism, Xian 710000, Peoples R China
[2] Acad Appl Studies Belgrade, Coll Tourism, Bulevar Zorana Dind 152a, Belgrade 11070, Serbia
[3] Univ East Sarajevo, Fac Transport & Traff Engn, Vojvode Misica 52, Doboj 74000, Bosnia & Herceg
[4] Govt Brcko Dist Bosnia & Herzegovina, Dept Publ Safety, Bulevara Mira 1, Brcko 76100, Bosnia & Herceg
关键词
fuzzy-rough approach; FRN SWARA; FRN ARAS; green supplier; furniture manufacturing; MODEL;
D O I
10.3390/su151310745
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Green supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy-rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy-rough numbers (FRN). The integrated FRN SWARA-FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy-rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model.
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页数:21
相关论文
共 67 条
[1]  
Aalic I., 2020, Decision Making: Applications in Management and Engineering, V3, P126, DOI [10.31181/dmame2003114d, DOI 10.31181/DMAME2003114D]
[2]  
Ali Z., 2021, Reports in Mechanical Engineering, V2, P105, DOI [10.31181/rme2001020105t, DOI 10.31181/RME2001020105T]
[3]  
Attaullah, 2023, SCI REP-UK, V13, P6676, DOI DOI 10.1038/s41598-023-28722-w
[4]  
Aydin S., 2020, ADV INTELLIGENT SYST, P1029, DOI [10.1007/978-3-030-23756-1_67, DOI 10.1007/978-3-030-23756-1_67]
[5]  
Aytekin A., 2021, Decision Making: Applications in Management and Engineering, V4, P1, DOI [DOI 10.31181/DMAME210402001A, 10.31181/dmame210402001a]
[6]  
Badi I., 2023, J. Intell. Manag. Decis, P66, DOI [10.56578/jimd020203, DOI 10.56578/JIMD020203]
[8]   Green supply chain management: Pressures, practices, and performance-An integrative literature review [J].
Balon, Virendra .
BUSINESS STRATEGY AND DEVELOPMENT, 2020, 3 (02) :226-244
[9]  
Biswas S., 2023, J. Decis. Anal. Intell. Comput., V3, P15, DOI [10.31181/10023022023b, DOI 10.31181/10023022023B]
[10]   Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model [J].
Bouraima, Mouhamed Bayane ;
Qiu, Yanjun ;
Stevic, Zeljko ;
Simic, Vladimir .
SOCIO-ECONOMIC PLANNING SCIENCES, 2023, 86