论文标题
统一的贝叶斯框架多标准决策问题
Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
论文作者
论文摘要
本文介绍了贝叶斯框架，以解决多标准决策（MCDM）问题的各个方面，从而利用了对MCDM方法和挑战的概率解释。通过利用贝叶斯模型的灵活性，提出的框架为MCDM中的关键挑战提供了统计优雅的解决方案，例如团体决策问题和标准相关性。此外，这些模型可以适应决策者（DMS）偏好（包括正常和三角形分布）的各种形式的不确定性，以及间隔偏好。为了解决大规模组MCDM方案，开发了概率混合模型，从而鉴定了DMS的均匀亚组。此外，设计了一种概率排名方案，以评估基于DM偏好的标准和替代方案的相对重要性。通过对各种数值示例的实验，对所提出的框架进行了验证，证明了它们的有效性，并突出了其与替代方法相比的区别特征。
This paper introduces Bayesian frameworks for tackling various aspects of multi-criteria decision-making (MCDM) problems, leveraging a probabilistic interpretation of MCDM methods and challenges. By harnessing the flexibility of Bayesian models, the proposed frameworks offer statistically elegant solutions to key challenges in MCDM, such as group decision-making problems and criteria correlation. Additionally, these models can accommodate diverse forms of uncertainty in decision makers' (DMs) preferences, including normal and triangular distributions, as well as interval preferences. To address large-scale group MCDM scenarios, a probabilistic mixture model is developed, enabling the identification of homogeneous subgroups of DMs. Furthermore, a probabilistic ranking scheme is devised to assess the relative importance of criteria and alternatives based on DM(s) preferences. Through experimentation on various numerical examples, the proposed frameworks are validated, demonstrating their effectiveness and highlighting their distinguishing features in comparison to alternative methods.
