Abstract
Due to the continuous cost reduction and technological innovation, electronic products are easily accessible to individuals and become omnipresent in our day-to-day lives. For electronics manufacturing enterprises, the pressure to maintain a competitive edge and the urgency to implement sustainable development makes the optimal selection of green supplier a necessity during operations. Although some studies have given some methods for green supplier selection, the consensus problem has not been sufficiently studied in the existing methods. In order to solve this problem, we propose a group consensus decision making framework to help choosing the best green supplier for electronics manufacturing. Firstly, decision makers’ preferences between the alternatives considered with respect to each criterion are elicited by the paired comparison method, and expressed as probabilistic linguistic preference relations. Secondly, a new consistency measure is defined and a convergent algorithm is described to aid the consistency improvement process in a given probabilistic linguistic preference relation. The algorithm adopts a local revision strategy and can be easily interpreted. Further, for the consensus model, in each round of this dynamic and interactive model, the most incompatible expert is asked to revise his/her judgment according to judgment feedback mechanism. If the expert rejects the suggestion, his/her weight of importance will be decreased based on the weighting feedback mechanism. Finally, a case study of selecting green supplier is provided to illustrate the application of the proposed group consensus decision making framework.
| Original language | English |
|---|---|
| Article number | 118781 |
| Journal | Journal of Cleaner Production |
| Volume | 245 |
| DOIs | |
| Publication status | Published - 1 Feb 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Consensus decision making
- Consistency
- Green supplier selection
- Probabilistic linguistic preference relations
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