Multicriteria Group Decision-Making Under Uncertainty Using Interval
  Data and Cloud Models
  In this study, we propose a multicriteria group decision making (MCGDM)
algorithm under uncertainty where data is collected as intervals. The proposed
MCGDM algorithm aggregates the data, determines the optimal weights for
criteria and ranks alternatives with no further input. The intervals give
flexibility to experts in assessing alternatives against criteria and provide
an opportunity to gain maximum information. We also propose a novel method to
aggregate expert judgements using cloud models. We introduce an experimental
approach to check the validity of the aggregation method. After that, we use
the aggregation method for an MCGDM problem. Here, we find the optimal weights
for each criterion by proposing a bilevel optimisation model. Then, we extend
the technique for order of preference by similarity to ideal solution (TOPSIS)
for data based on cloud models to prioritise alternatives. As a result, the
algorithm can gain information from decision makers with different levels of
uncertainty and examine alternatives with no more information from
decision-makers. The proposed MCGDM algorithm is implemented on a case study of
a cybersecurity problem to illustrate its feasibility and effectiveness. The
results verify the robustness and validity of the proposed MCGDM using
sensitivity analysis and comparison with other existing algorithms.
