Mixed strategy to allocate resources with air pollution treatment in China: based on the analytic network process and large-group decision-making method

Chen X., Zhao L., Ozdemir M., Liang H.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, vol.25, no.17, pp.16885-16899, 2018 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 17
  • Publication Date: 2018
  • Doi Number: 10.1007/s11356-018-1826-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.16885-16899
  • Keywords: Air pollution, Resource allocatio, Analytic network process, Large-group decision-making, Decision weight, Alternative selection, MANAGEMENT, SELECTION, ANP, QUALITY, MODEL, ALTERNATIVES, TECHNOLOGIES, TURKEY, TOPSIS, CITY
  • Eskisehir Osmangazi University Affiliated: Yes


The resource allocation of air pollution treatment in China is a complex problem, since many alternatives are available and many criteria influence mutually. A number of stakeholders participate in this issue holding different opinions because of the benefits they value. So a method is needed, based on the analytic network process (ANP) and large-group decision-making (LGDM), to rank the alternatives considering interdependent criteria and stakeholders' opinions. In this method, the criteria related to air pollution treatment are examined by experts. Then, the network structure of the problem is constructed based on the relationships between the criteria. Further, every participant in each group provide comparison matrices by judging the importance between criteria according to dominance, regarding a certain criteria (or goal), and the geometric average comparison matrix of each group is obtained. The decision weight of each group is derived by combining the subjective weight and the objective weight, in which the subjective weight is provided by organizers, while the objective weight is determined by considering the consensus levels of groups. The final comparison matrices are obtained by the geometric average of comparison matrices and the decision weights. Next, the resource allocation is made according to the priorities of the alternatives using the super decision software. Finally, an example is given to illustrate the use of the proposed method.