A cloud‐based and web‐based group decision support system in multilingual environment with hesitant fuzzy linguistic preference relations. Issue 8 (28th December 2021)

• Record Type: Journal Article
• Authors: Liu, Zixu; Han, Junyi; Meng, Fanlin; Liao, Huchang
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 8(2022)
• Page Start: 5186
• Page End: 5216
• Publication Date: 2021-12-28
• Subjects: cloud computing -- decision support system -- group decision making -- hesitant fuzzy linguistic preference relation -- pod‐based architecture; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22789
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Due to the growing needs in decision‐making under uncertainty, existing studies introduced consistency and consensus‐driven algorithms for group decision‐making (GDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs). A decision support system (DSS) that can host these GDM algorithms to provide decision‐support services or tools for practical use is urgently needed. However, the state‐of‐the‐art architectures cannot organize these algorithms and related data to run within one framework. This is mainly due to the running environments for these GDM algorithms are different since these algorithms were not originally designed to be compatible. Given the multilingual consistency and consensus‐based decision support algorithms, how to design and implement a cloud‐based DSS in a multilingual environment is still an open question. To fill this gap, this paper provides a web‐based and cloud‐based DSS with a novel architecture that utilizes the advantages of microservices. The proposed system implements a multilingual support framework to dynamically upload, manage and run multilingual consistency and consensus‐based decision support algorithms. An algorithm recommendation module is developed to help users choose suitable decision support algorithms. Tokenization is applied to deal with regulatory issues of knowledge protection, data privacy, and security while storing, analyzing, and transforming data into different algorithms for effective decision‐making. An expert feedback study verified that our web and cloud‐based DSS is a right artifact to fulfill the objective claimed in this paper.