A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem. (April 2021)

• Record Type: Journal Article
• Authors: Jana, Chiranjibe; Pal, Madhumangal
• Is Part Of: Engineering applications of artificial intelligence. Volume 100(2021)
• Publication Date: 2021-04
• Subjects: Dynamic hybrid multiple attribute decision making -- Dynamic intuitionistic (interval-valued intuitionistic fuzzy) fuzzy Dombi weighted aggregation operators -- GRA approach; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104203
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This article intends to study the dynamic hybrid multi-attribute decision making (DHMADM) process, where the choice information is given in two-scale intuitionistic fuzzy numbers (IFNs) as well as interval-valued intuitionistic fuzzy numbers (IVIFNs) by the decision-makers (DMs) at distinct periods. We have contributed some new dynamic weighted aggregation operators (AOs), namely dynamic intuitionistic fuzzy Dombi weighted average (DIFDWA), weighted geometric (DIFDWG) operators, and uncertain dynamic intuitionistic fuzzy Dombi weighted average (UDIFDWA), weighted geometric (UDIFDWG) operators for interval uncertainty. Next, we have obtained the aggregated data by using these dynamic Dombi operators. Then, we apply two grey relational analysis (IF-GRA and IVIF-GRA) approach on the aggregated matrix to obtain the overall grey relational degree for each option from positive and negative ideal (PIA and NIA) alternative. Then, we evaluate each choice's relative relational degree from the positive to get the final rank. Finally, we demonstrate the proposed model with a numerical example to compare the proposed model for applicability and validity. Highlights: A new GRA approach is developed in connect with dynamic aggregation operators. We utilize the proposed approach for dynamic hybrid multi-attribute decision making. A case study is provided to demonstrate the proposed model by an numerical example. A comparative analysis is performed for the proposed study.