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17-09-2022

Green Supplier Selection Mechanism Based on Information Environment of Z-Numbers

Authors: Congjun Rao, Mingyun Gao, Mark Goh, Xinping Xiao

Published in: Cognitive Computation | Issue 2/2023

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Abstract

In the green procurement management of firms, by rigorously evaluating and selecting suppliers, firms can effectively reduce the operation risk and improve the sustainable value-added capacity of supply chain. This paper designs a novel green supplier selection mechanism based on an uncertain information environment. First, a new green supplier evaluation index system is designed by considering the economic, environmental, and social drivers from the perspective of sustainable development. Then, the Z-numbers are applied to describe the fuzziness degree of the decision information and the reliability degree of the fuzzy attribute values. We re-define the possibility degree of trapezoidal fuzzy numbers, and on that basis, we define the possibility degree of Z-numbers. A ranking method based on the possibility degree of Z-numbers is applied to select the green suppliers. Finally, the implementation, applicability, and feasibility of the proposed mechanism are highlighted by providing a decision-making example of green supply selection together with the comparison analysis with the existing methods. From the comparison analysis and discussion, the information processing method in our proposed mechanism can effectively avoid the information loss caused by the direct aggregation of fuzzy information, which shows that the proposed mechanism is more feasible and effective than other congeneric methods. The results suggest that the proposed mechanism of green supplier selection can handle multi-attribute decision-making problems in an uncertain cognitive information environment with Z-numbers, consistent with human cognition.

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Metadata
Title
Green Supplier Selection Mechanism Based on Information Environment of Z-Numbers
Authors
Congjun Rao
Mingyun Gao
Mark Goh
Xinping Xiao
Publication date
17-09-2022
Publisher
Springer US
Published in
Cognitive Computation / Issue 2/2023
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10055-x

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