A Group Multi-Criteria Decision-Making Approach Based on the Best-Only Method for Cloud Service Selection

The evaluation of cloud services from various providers involves assessing multiple criteria,creating a multi-criteria decision-making (MCDM) problem. Group decision-making among experts addscomplexity to this process. Traditional methods like AHP and BWM are effective but burdensome due toextensive pairwise comparisons, computational demands, and inconsistency. Thus, there is a clear need fora more efficient and reliable approach that reduces comparison efforts, ensures consistency, and improvesoverall decision-making efficiency, crucial for enhancing cloud service selection tailored to user needs. Thebest-only method (BOM) simplifies decision-making by considering a single decision-maker's preferences,but it fails to address group decision-making complexities. This paper introduces the group BOM (GBOM),which aggregates criteria/alternative weights using probability and statistical techniques across multipledecision-makers (DMs). The GBOM method was validated with three numerical examples, demonstratingconsistent criteria rankings compared to existing AHP and BWM group-based MCDM methods, with aconstant consistency ratio (CR) of zero and lower computational complexity requiring onlyn-1 comparisonscompared to BWM's 2n-3 and AHP'snx(n-1)/2. Furthermore, GBOM was applied to a real-worldcloud service selection case study, showcasing improved consistency (CR=0), reduced expert comparisons,and a novel approach to ranking cloud services based on group preferences using the best-only method.The proposed GBOM method offers a robust and efficient solution for MCDM in cloud service selection,addressing critical limitations of existing methodologies.