Once an emergency event (EE) happens, emergency decision-making (EDM) plays a key role in mitigating the loss. EDM is a complex problem. Compared with conventional decision-making problems, more experts participate in decision-making. It usually has the feature of large group emergency decision-making (LGEDM). This paper proposes a large group emergency decision-making method based on Bayesian theory, relative entropy, and Euclidean distance, which is used for large group emergency decision-making with uncertain probabilities of occurrence, unknown attribute weights, and expert weights. In order to improve the accuracy of decision-making, Bayesian method is introduced into the calculation of scenario probability in the process of LGEDM. In the decision-making process, the experts’ risk preference is considered. The experts’ decision preference information is a symmetric and uniformly distributed interval value. The perceived utility values of the experts are obtained by introducing prospect theory. Euclidean distance is used to measure the contributions of experts to aggregation similarity, and different weights are given to experts according to their contributions. A relative entropy model with completely unknown weight information constraints is established to obtain attribute weights, which takes into account the differences of different alternatives under the same attribute and the differences between alternatives and the ideal solution. An example of nuclear power emergency decision-making illustrates the effectiveness of this method.
Probabilistic linguistic term set can effectively express the evaluation preferences of decision makers, and cloud model can combine fuzziness and randomness, describe the correlation between fuzziness and randomness with numerical features, and form a mutual mapping between qualitative and quantitative. For the multi-attribute decision problem with unknown expert weights and attribute weights under the probabilistic linguistic term set environment, this paper proposes a cloud model-based multi-attribute decision method. Firstly, golden section method is used to transform probabilistic linguistic information into digital features of clouds, and the definition of the probabilistic linguistic cloud and calculation formula of the distance between the probabilistic linguistic clouds are given. Secondly, the expert weights and attribute weights are calculated based on this distance formula, and the alternatives are ranked using the technique for order preference by similarity to ideal solution (TOPSIS). Finally, the proposed decision-making method is applied to the decision of public evacuation in nuclear accidents, and the feasibility of the proposed method is verified by comparative analysis.
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