Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy

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.