A Scientific Decision Framework for Cloud Vendor Prioritization under Probabilistic Linguistic Term Set Context with Unknown/Partial Weight Information

Symmetry

Dragan Pamucar¹  Samarjit Kar²  K.S. Ravichandran³  V. Sangeetha³  R. Krishankumar³  R. Sivagami³  Xiao-Zhi Gao⁴ 

[1] Department of Logistics, University of Defence in Belgrade Military Academy, 11000 Belgrade, Serbia;Department of Mathematics, National Institute of Technology, Durgapur 713209, West Bengal, India;School of Computing, SASTRA University, Thanjavur 613401, India;School of Computing, University of Eastern Finland, Kuopio FI-70211, Finland;

关键词: cloud vendors;    COPRAS method;    muirhead mean;    programming model and statistical variance;   

DOI  :  10.3390/sym11050682

来源: DOAJ

【 摘 要 】

With the tremendous growth of Cloud Vendors, Cloud vendor (CV) prioritization is a complex decision-making problem. Previous studies on CV selection use functional and non-functional attributes, but do not have an apt structure for managing uncertainty in preferences. Motivated by this challenge, in this paper, a scientific framework for prioritization of CVs is proposed, which will help organizations to make decisions on service usage. Probabilistic linguistic term set (PLTS) is adopted as a structure for preference information, which manages uncertainty better by allowing partial information ignorance. Decision makers’ (DMs) relative importance is calculated using the programming model, by properly gaining the advantage of the partial knowledge and attributes, the weights are calculated using the extended statistical variance (SV) method. Further, DMs preferences are aggregated using a hybrid operator, and CVs are prioritized, using extended COPRAS method under the PLTS context. Finally, a case study on CV prioritization is provided for validating the scientific framework and the results are compared with other methods for understanding the strength and weakness of the proposal.

【 授权许可】

Unknown