Linguistic multi-criteria decision-making aggregation model based on situational ME-LOWA and ME-LOWGA operators

Ching Hsue Cheng, Mu Yen Chen, Jing Rong Chang

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


The evaluated data with multiplicative or linguistic preferences should be aggregated with information fusion and aggregation that are important research topics in many fields, such as neural networks, fuzzy logic controllers, expert systems, group decision-making, and multi-criteria decision-making (MCDM). Ordered weighted averaging operators have been extensively adopted to handle MCDM problems. However, previous operators are usually independent of their situations and cannot reflect the change in decision situations. Besides, how to solve MCDM problems with feasible operators is still an interesting direction. To resolve above problems, a linguistic MCDM aggregation model is proposed in this paper, which contained two linguistic and situational operators, that is, situational maximal entropy linguistic ordered weighted averaging and maximal entropy linguistic ordered weighted geometric averaging operators. This proposed model has their ability to handle MCDM problems under different decision situations according to the decision-maker’s preference toward criteria. The proposed model is applied to two previous datasets, which are the evaluation of the best main battle tank and the best school in a university. The results of this paper indicate that the proposed model can deal with the situational group MCDM problems with linguistic or multiplicative and linguistic preferences based on proposed operators.

期刊Granular Computing
出版狀態Accepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 電腦科學應用
  • 人工智慧


深入研究「Linguistic multi-criteria decision-making aggregation model based on situational ME-LOWA and ME-LOWGA operators」主題。共同形成了獨特的指紋。