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A new emergency decision support methodology based on multi-source knowledge in 2-tuple linguistic model

机译:二元语言模型中基于多源知识的紧急决策支持新方法

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Knowledge is the foundation of emergency decision-making (EDM), in which the experts from multi fields express their knowledge with multi-granularity linguistic model to assistant decision-making. Thus, the paper proposed a new decision support methodology to generate decision-making knowledge. In this paper, the framework of decision knowledge generation in the EDM was introduced firstly. To generate decision-making knowledge accurately and objectively, two objective models, which can effectively determine the weights of criteria and experts respectively, were built based on the tacit knowledge hidden in the original information. Then, the personal knowledge, generated by combining the normalized decision knowledge and the weight vector of criteria, is further aggregated into the collective knowledge by means of aggregation operator. Finally, an illustrative example is presented to verify the application of the proposed methods, and relevant discussions prove the results obtained from the proposed decision support methodology can improve the scientificity and accuracy of the EDM. (C) 2018 Elsevier B.V. All rights reserved.
机译:知识是应急决策(EDM)的基础,其中来自各个领域的专家通过多粒度语言模型向辅助决策表达他们的知识。因此,本文提出了一种新的决策支持方法来生成决策知识。本文首先介绍了EDM中决策知识的生成框架。为了准确,客观地生成决策知识,在原始信息中隐含的隐性知识的基础上,建立了两个可以分别有效地确定标准和专家权重的客观模型。然后,将通过将归一化的决策知识和标准的权重向量相结合而生成的个人知识通过聚合算子进一步聚合为集体知识。最后,给出了一个说明性示例来验证所提出方法的应用,相关讨论证明了从所提出的决策支持方法中获得的结果可以提高EDM的科学性和准确性。 (C)2018 Elsevier B.V.保留所有权利。

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