Affiliations: [a] Data Recovery Key Lab of Sichuan Province, Neijiang Normal University, Neijiang, Sichuan, P.R. China
| [b] School of Computing, Ulster University, Jordanstown Campus, Northern Ireland, UK
| [c] School of Mathematics and Information Science, Neijiang Normal University, Neijiang, Sichuan, P.R. China
Correspondence:
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Corresponding author. Liu Yi, Data Recovery Key Lab of Sichuan Province, Neijiang Normal University, Neijiang 641000, Sichuan, P.R. China. E-mail: liuyi@njtc.edu.cn.
Abstract: The present work is focused on dynamic intuitionistic fuzzy multi-attribute decision making (DIF-MADM) problem, while "dynamic" means the decision-related information may be collected at different periods, a situation commonly happened in many of real world MADM problems. After the review and analysis of some drawbacks on the existing DIF-MADM methods, on the one hand, we propose a new DIF-MADM method based on the evidential reasoning (ER) algorithm in order to address some of those limits; on the other hand, and a new dynamic intuitionistic fuzzy weighted geometric (DIFWG) operator is introduced, named modified dynamic intuitionistic fuzzy weighted geometric (MDIFWG) operator, then a MDIFWG-based DIF-MADM method is also proposed to address some other limits of the existing methods. Some numerical examples are provided to illustrate the practicality and feasibility of the proposed two methods through, the comparative analysis with the existing DIF-MADM methods, along with some sensitivity analyses also carried out to analyse the distinct features of the proposed methods.
