论文标题
使用间隔数据和云模型在不确定性下进行多准则组决策
Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models
论文作者
论文摘要
在这项研究中，我们提出了一个多准则组决策（MCGDM）算法，在不确定性下，将数据收集为间隔。提出的MCGDM算法汇总了数据，确定标准的最佳权重，并在没有进一步输入的情况下对替代方案进行排名。间隔为专家提供了针对标准评估替代方案的灵活性，并提供了获得最大信息的机会。我们还提出了一种新的方法，可以使用云模型汇总专家判断。我们引入了一种实验方法来检查聚合方法的有效性。之后，我们将聚合方法用于MCGDM问题。在这里，我们通过提出双重优化模型来找到每个标准的最佳权重。然后，我们扩展了基于云模型的数据的相似性（TOPSIS）的相似性（TOPSIS）的偏好顺序，以确定替代方案的优先级。结果，算法可以从不同级别的不确定性的决策者那里获取信息，并检查替代方案，而没有更多的决策者信息。提出的MCGDM算法是在网络安全问题的案例研究中实施的，以说明其可行性和有效性。结果通过灵敏度分析和与其他现有算法进行比较来验证提出的MCGDM的鲁棒性和有效性。
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.
