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Title Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy
ID_Doc 3308
Authors Tang, M; Liao, HC
Title Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy
Year 2021
Published
DOI 10.1016/j.techfore.2021.120719
Abstract The circular economy is a concept that emphasizes a sustainable and regenerative method of business operations. The circular economy has become the economic embodiment and inevitable choice for the implementation of sustainable development strategies. For many circular economy activities such as the selection of pilot parks or cities, many experts from multiple fields or ministries are often invited to make decisions according to multiple attributes. Hence, to solve such problems, it is necessary to develop an efficient multiattribute large-scale group decision-making model that can facilitate coordination of a large group of experts. First, a natural language processing technique from a specific data mining application field is adopted to mine public preference information. Then, experts are clustered and subgroup leaders are selected. Next, a consensus reaching model is proposed to reduce the discrepancies among experts. Finally, an illustrative example regarding the selection of pilot eco-industrial parks in the Sichuan Province, China, is given to demonstrate the applicability of the proposed model. The results show that our model can effectively address evaluation problems of circular economy activities involving a large group of experts.
Author Keywords Circular economy; Large-scale group decision making; Data mining; Subgroup leader; Eco-industrial parks
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Social Science Citation Index (SSCI)
EID WOS:000637775800007
WoS Category Business; Regional & Urban Planning
Research Area Business & Economics; Public Administration
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