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 adds complexity to this process. Traditional methods like AHP and BWM are effective but burdensome due to extensive pairwise comparisons, computational demands, and inconsistency. Thus, there is a clear need for a more efficient and reliable approach that reduces comparison efforts, ensures consistency, and improves overall decision-making efficiency, crucial for enhancing cloud service selection tailored to user needs. The best-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 multiple decision-makers (DMs). The GBOM method was validated with three numerical examples, demonstrating consistent criteria rankings compared to existing AHP and BWM group-based MCDM methods, with a constant consistency ratio (CR) of zero and lower computational complexity requiring only $n-1$ comparisons compared to BWM’s $2n-3$ and AHP’s ${n\mathrm {\times (}n\mathrm {-1)}} \mathord {\left /{{\vphantom {{n\mathrm {\times (}n\mathrm {-1)}} 2}}}\right. \hspace {-1.2pt} } 2$ . Furthermore, GBOM was applied to a real-world cloud 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.