Title of article :
Assessing suppliers in green supply chain based on a group compromise solution approach with interval-valued 2-tuple linguistic information
Author/Authors :
Foroozesh, Nazanin Department of Industrial Engineering and Management Systems - Amirkabir University of Technology , Karimi, Behrooz Department of Industrial Engineering and Management Systems - Amirkabir University of Technology , Mousavi, Meysam Department of Industrial Engineering - Faculty of Engineering - Shahed University
Pages :
17
From page :
249
To page :
265
Abstract :
The concept regarding green supply chain management developed as a response in order to increase public awareness from environmental stability which regularly holds both the environment and supply chain management. In the past few years, green supply chain management has become a challenging issue in advanced operations research. In a manufacturing company, selecting an appropriate supplier for their long-term growth prospects by the supply chain managers not only proceed with the business strategy but also help the company remain in a competing position. Considering multiple criteria group decision-making (MCGDM) problem in order to solve the green supplier selection (GSS) includes many unmeasurable and contradictory criteria. This research presents a new group decision approach via interval-valued 2-tuple linguistic preferences and compromises solution to appraise the GSP. The falsification and loss of information which happens formerly in the probabilistic linguistic information process are avoided. Next, a real application is provided via the introduced approach under uncertain conditions regarding the recent literature in the manufacturing industry. Finally, for validation, sensitivity and comparative analyses and a discussion on the effectiveness and benefits of the introduced method are completed and provided.
Keywords :
Interval valued 2-Tuple information , Linguistic Preferences , Group compromise solution Model , Assessing green suppliers
Journal title :
Journal of Quality Engineering and Production Optimization
Serial Year :
2020
Record number :
2629396
Link To Document :
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