A grey-based green supplier selection model for uncertain environments

JOURNAL OF CLEANER PRODUCTION

卷:221

Article

Haeri, Seyed Amin Seyed¹  Rezaei, Jafar² 

[1] Clemson Univ, Coll Business, Dept Management, 100 Sirrine Hall, Clemson, SC 29634 USA

[2] Delft Univ Technol, Fac Technol Policy & Management, NL-2628 BX Delft, Netherlands

关键词: Green supplier selection;    Best-worst method;    Grey relational analysis;    Cognitive maps;   

DOI  :  10.1016/j.jclepro.2019.02.193

来源: Elsevier

【 摘 要 】

The concept of green supply chain management emerged as a response to increasing public awareness of environmental protection in the past few decades. As companies tend to outsource a variety of their activities, green supplier selection as an imperative function of green supply chain management, has a crucial role in helping companies to maintain their strategic competitiveness. Despite the plethora of studies introducing supplier selection models based on economic criteria, studies that take into account the environmental issues are rather limited. In this study, a comprehensive grey-based green supplier selection model is proposed that incorporates both economic and environmental criteria. A novel weight assignment model is proposed by combining best-worst method and fuzzy grey cognitive maps to capture the interdependencies among the criteria. Improved grey relational analysis is advanced to be able to use grey weights of criteria to evaluate green suppliers which are subsequently ranked using an interval analysis approach. This study contributes to the decision-making theory by addressing the shortcomings of the available green supplier selection models. A real-world case study is also presented to show the applicability and effectiveness of the proposed model. The results of this study proved the proposed comprehensive model to be well capable of addressing the green supplier selection problem by taking in to account the interdependencies between criteria as well as the uncertainties associated with experts' judgments. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free