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A multistage decision-making method for multi-source information with Shapley optimization based on normal cloud models
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-07-15 , DOI: 10.1016/j.asoc.2021.107716
Weiqiao Liu 1 , Jianjun Zhu 1
Affiliation  

It is difficult to make scientific decisions for multistage complex decision-making problems, which are heavily affected by multi-level attributes, stage changes, and multi-source heterogeneous information. The normal cloud model is an uncertainty transformation model for constructing mappings between numerical values and their linguistic representations, which can be used to describe multi-source evaluation information for a better reflection of the distribution characteristics and uncertainties. This study aims to develop an effective multi-level multi-attribute decision-making method based on normal cloud models to solve multistage evaluation problems with multi-source heterogeneous information. Firstly, an optimization model is established to capture stage weights and lower-level attribute weights based on cloud distance. Secondly, a bidirectional cloud projection measure is proposed to obtain the measurement values of upper-level attributes based on horizontal and vertical reference points. Thirdly, the comprehensive evaluation values of alternatives are obtained based on Shapley weights of the upper-level attributes. Finally, an illustrative example is presented to clarify the feasibility and superiority of the proposed method. The result indicates that our method is (1) a flexible evaluation framework based on target reference points in fuzzy environments, (2) a powerful measurement tool for aggregating multi-source information, and (3) an objective decision-making method considering the interrelationships between objects. It is of great significance for the enterprises to optimize their operation mechanisms and resource allocation based on these evaluation results.



中文翻译:

基于正态云模型的多源信息多阶段决策方法与沙普利优化

多阶段复杂决策问题,受多层次属性、阶段变化、多源异构信息影响,难以科学决策。正态云模型是一种构建数值与其语言表示之间映射的不确定性转换模型,可用于描述多源评价信息,以更好地反映分布特征和不确定性。本研究旨在开发一种有效的基于常态云模型的多层次多属性决策方法,以解决多源异构信息的多阶段评价问题。首先,建立优化模型以捕获基于云距离的阶段权重和较低级别的属性权重。第二,提出了一种基于水平和垂直参考点的双向云投影测量方法来获取上层属性的测量值。第三,基于上层属性的Shapley权重得到备选方案的综合评价值。最后,给出了一个说明性例子,以阐明所提出方法的可行性和优越性。结果表明,我们的方法是(1)基于模糊环境中目标参考点的灵活评估框架,(2)聚合多源信息的强大测量工具,以及(3)考虑相互关系的客观决策方法对象之间。根据这些评价结果,对企业优化经营机制和资源配置具有重要意义。

更新日期:2021-07-23
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