Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization

Recently, large-scale group decision making (LSGDM) in social network comes into being. In the practical consensus of LSGDM, the unit adjustment cost of experts is difficult to obtain and may be uncertain. Therefore, the purpose of this paper is to propose a consensus model based on robust optimization. This paper focuses on LSGDM, considering the social relationship between experts. In the presented model, an expert clustering method, combining trust degree and relationship strength, is used to classify experts with similar opinions into subgroups. A consensus index, reflecting the harmony degree between experts, is devised to measure the consensus level among experts. Then, a minimum cost model based on robust optimization is proposed to solve the robust optimization consensus problem. Subsequently, a detailed consensus feedback adjustment is presented. Finally, a case study and comparative analysis are provided to verify the validity and advantage of the proposed method. © 2020 Elsevier Inc.

Авторы
Lu Y. 1 , Xu Y.1 , Herrera-Viedma E. 2, 3 , Han Y.4
Журнал
Издательство
Elsevier Inc.
Язык
Английский
Страницы
910-930
Статус
Опубликовано
Том
547
Год
2021
Организации
  • 1 Business School, Hohai University, Nanjing, 211100, China
  • 2 Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, 18071, Spain
  • 3 Peoples’ Friendship, University of Russia (RUDN University), Moscow, Russian Federation
  • 4 Business school, University of Shanghai for Science and Technology, Shanghai, China
Ключевые слова
Consensus model; Large-scale group decision making; Robust optimization; Social network
Дата создания
20.04.2021
Дата изменения
20.04.2021
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/71890/