Applied Soft Computing ( IF 5.472 ) Pub Date : 2020-10-16 , DOI: 10.1016/j.asoc.2020.106801 Sivagami Ramadass; Raghunathan Krishankumar; Kattur Soundarapandian Ravichandran; Huchang Liao; Samarjit Kar; Enrique Herrera-Viedma
As IT industries grow at a faster pace, cloud technology becomes inevitable. Attracted by the scope, many cloud vendors (CVs) arise. A rational/systematic selection is an urge to tackle the scalability of CVs. To circumvent the issue, in this paper, a framework is proposed for CV selection under with probabilistic linguistic term sets (PLTSs). The PLTS is a flexible structure that allows partial ignorance of occurring probabilities. Initially, attributes’ weights are calculated using a programming model, which uses partial information effectively. Later, decision-makers’ (DMs’) weights are computed by integrating evidence theory with Bayes approximation. Preferences from DMs are aggregated by proposing a two-way operator, which aggregates linguistic preferences using the rule-based method and occurring probabilities using Maclaurin symmetric mean. Moreover, CVs are ranked by using an integrated PROMETHEE–Borda method under the PLTS. Further, to test the validity of the framework, a case study on CV selection is presented for a small-scale company. Finally, the advantages and limitations of the proposed framework are investigated by comparison with other methods and the results infer that (i) the proposed framework is 63.67% robust even after adequate changes are made to the alternatives; (ii) the proposed framework is 87.67% robust even after adequate changes are made to the attributes; (iii) from partial adequacy test, the robustness is determined as 77.67% and 92.33%; and (iv) from the broadness test, the proposed framework produces an average deviation of 9% among their rank values, which is better than the extant models that produce an average deviation close to 7.8%.