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A novel approach for enhancing green supply chain management using converged interval-valued triangular fuzzy numbers-grey relation analysis

A novel approach for enhancing green supply chain management using converged interval-valued triangular fuzzy numbers-grey relation analysis

Tseng, Ming-Lang, Lim, Ming, Wu, Kuo-Jui, Zhou, Li ORCID: 0000-0001-7132-5935 and Bui, David Tat Dat (2017) A novel approach for enhancing green supply chain management using converged interval-valued triangular fuzzy numbers-grey relation analysis. Resources, Conservation and Recycling, 128. pp. 122-133. ISSN 0921-3449 (doi:https://doi.org/10.1016/j.resconrec.2017.01.007)

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Abstract

The existing literatures are lacking on the cost and benefit concerns, screening the measures and convergence of interval-valued triangular fuzzy numbers-grey relation analysis (IVTFN-GRA) weight together. Nonetheless, Green supply chain management is always suffering the linguistic preferences and system incomplete information in evaluation process to enhance the performance. Yet, those previous studies are merely based on un-converged weight results. Hence, this study proposed a hybrid method to dealing with this multi-criteria evaluation problem. Fuzzy Delphi method proposes to screen the evaluation criteria and converged IVTFN-GRA weight method handles the vagueness system uncertainty and incomplete information with interdependence relations. Hence, the proposed hybrid method enhanced the green supply chain management and compared multi-methods to enhance their performance in Taiwanese electronic focal firm. The result showed that the converged weight is consistent with the real practices, despite the differences with the current average weighting method. The finding in the long-term perspective: resilience and operational improvement are the top weighted aspects.

Item Type: Article
Uncontrolled Keywords: Converged interval-valued triangular fuzzy numbers-grey relation analysis; Green supply chain management
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Related URLs:
Last Modified: 12 Jun 2019 00:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT e
Selected for GREAT 2019: GREAT 5
URI: http://gala.gre.ac.uk/id/eprint/16281

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