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Energies
Article . 2022 . Peer-reviewed
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Energies
Article . 2022
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Supplier Evaluation Considering Green Production Based on Probabilistic Linguistic Information

Authors: Shuailei Yuan; Aijun Liu; Zengxian Li; Yun Yang; Jing Liu; Yue Su;

Supplier Evaluation Considering Green Production Based on Probabilistic Linguistic Information

Abstract

The evaluation of manufacturing component suppliers is focused on economic indicators, with insufficient emphasis on green indicators and no consideration of the correlation between indicators. Firstly, indicators related to green production are incorporated into the supplier evaluation system. Then, for the problem that attributes in decision making can be divided into different categories and there are interrelationships between attributes of the same category, a multi-attribute decision-making (MADM) method based on the partitioned Maclaurin symmetric mean operator (PMSM) is proposed. Finally, the proposed MADM method was applied to the evaluation of component suppliers considering green production. Comparing popular decision methods with the newly proposed method for validation, it was demonstrated that the proposed multi-attribute decision method is highly flexible and versatile. Furthermore, the newly proposed aggregation operator can not only handle the correlation between multiple attributes, but also be converted to other general aggregation operators through parameter adjustment.

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Keywords

Technology, probabilistic linguistic weighted partitioned Maclaurin symmetric mean operator (<i>PLWPMSM</i>), manufacturing industry, T, supplier evaluation, supplier evaluation; greener production; probabilistic linguistic weighted partitioned Maclaurin symmetric mean operator (<i>PLWPMSM</i>); multi-attribute decision making; manufacturing industry, multi-attribute decision making, greener production

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold