Decision-makers (DMs) will face severe challenges when selecting optimal alternative for emergency response over multiple time periods. The aim of this paper is to develop a novel dynamic emergency decision-making method with probabilistic hesitant fuzzy information for handling emergencies. Firstly, an approach based on GM(1,1) model for predicting the decision-making information at the next stage is proposed. Secondly, a new probabilistic hesitant fuzzy distance measure based on the hesitant degree of probabilistic hesitant fuzzy element (PHFE) is put forward, and a mathematical programming model to determine the stage weights is established. What’s more, the closeness degree between each alternative and ideal alternative is calculated and the emergency alternatives are ranked on the strength of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. Moreover, a practical example is used to verify the feasibility and rationality of the proposed method.

In recent years, with the uninterrupted development of sharing accommodation, it not only caters to the diversified accommodation of tourists, but also takes an active role in expanding employment and entrepreneurship channels, enhancing the income of urban and rural residents, and promoting the revitalization of rural areas. However, with the continuous expansion of the scale of sharing accommodation, it is fairly complicated for users to search appropriate services or information. The decision-making problems become more and more complicated. Hence, a probabilistic hesitant fuzzy recommendation decision-making algorithm based on bipartite network projection is proposed in this paper. First of all, combining the users’ decision-making information and the experts’ evaluation information, a bipartite graph connecting users and alternatives is established. Then, the satisfaction degree of probabilistic hesitant fuzzy element is defined. Besides, the recommended alternative is obtained by the allocation of resources. Finally, a numerical case of Airbnb users is given to illustrate the feasibility and effectiveness of the proposed method.

A Best-Worst multi-attribute decision-making (MADM) method based on a new possibility degree is put forward to deal with MADM problems with probabilistic linguistic evaluation information. Firstly, a new possibility degree for pairwise comparisons with probabilistic linguistic term sets (PLTSs) is defined. Secondly, starting from the new possibility degree, two different ideas of Best-Worst Method (BWM) for getting the optimal attribute weights are put forward. Thirdly, combining the new probabilistic linguistic possibility degree and the two BWM ideas, two optimization models for determining the attribute weights are constructed, respectively. Moreover, consistency ratios for two new BWM models are proposed to check the reliability of the pairwise comparisons. Meanwhile, the state of optimal solutions for the new BWM models is analyzed. Finally, a new Best-Worst MADM method under probabilistic linguistic information is presented, which is applied to a practical example of selecting optimal green enterprises. Some comparative analyses are given to show the rationality and validity of the proposed method.