Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models

TL;DR

This paper introduces a novel multicriteria group decision-making algorithm that handles interval data and uncertainty using cloud models, enabling effective aggregation, weighting, and ranking of alternatives without extensive additional input.

Contribution

It proposes a new method combining interval data and cloud models for group decision-making, including an innovative aggregation technique and a bilevel optimization approach for criteria weighting.

Findings

Algorithm effectively handles uncertainty and interval data.

Case study demonstrates robustness and validity.

Sensitivity analysis confirms reliability.

Abstract

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.