An integrated group decision‐making framework for selecting cloud service providers based on regret theory and EVAMIX with hybrid information. Issue 6 (5th October 2021)

• Record Type: Journal Article
• Authors: Liu, Zhengmin; Wang, Di; Wang, Wenxin; Liu, Peide
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 6(2022)
• Page Start: 3480
• Page End: 3513
• Publication Date: 2021-10-05
• Subjects: cloud service provider selection -- EVAMIX -- group decision‐making method -- hybrid information -- regret theory; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22698
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Handing computing assets to cloud service providers (CSPs) to obtain cloud services is one of the important strategies for enterprises to embrace the digital era, and CSP selection is a crucial decision‐making process for cloud deployment. However, there are many criteria involved in selecting an optimal CSP, not all of which can be accurately quantified. Therefore, CSP selection is a typical hybrid‐information decision‐making problem, in which criterion evaluation values are expressed in various forms. Meanwhile, the psychological behavior of the CSP selection team also has a significant impact on the decision‐making result, which is poorly considered in the existing research results on CSP selection. Thus, in this paper, a new group decision‐making support framework incorporating regret theory is constructed to select CSPs with hybrid information. Initially, various forms of hybrid information are processed separately to avoid the distortion of heterogeneous information caused by traditional conversion methods. Then, considering the psychology of regret aversion, the respective regret–rejoice functions for hybrid information are defined. Subsequently, regret–rejoice values are introduced into the evaluation of mixed data method framework, and a decision‐making support procedure based on it is established, in which an expert weight determination method based on the maximizing consensus model is proposed, and the group best–worst method is used to calculate criteria weights. Afterwards, an illustrative example of CSP selection is given to clarify the implementation process of the proposed method. Finally, the effectiveness and superiority of the proposed decision‐making framework in selecting CSPs are explained through parameter analysis and comparison with existing methods.