An Emergency Decision-Making Method for Probabilistic Linguistic Term Sets Extended by D Number Theory

A probabilistic linguistic opinion dynamics method based on the DeGroot model for emergency decision-making in response to COVID-19

Emergency decision-making entails a multi-criteria problem with a short period and urgent events, which creates difficulties for decision makers to undertake an optimal decision. To ensure the validity and rationality of decision results, the probabilistic linguistic term set is adopted to represent the evaluation information of experts because it can assign different probabilities or importance to different linguistic terms, which is closely related to human cognition. In addition, to portray the dynamic changes in the emergency decision-making process, this study develops a new dynamics method based on the DeGroot model with probabilistic linguistic information. First, to simulate the transition matrix of probabilistic linguistic opinions, the basic operational rules are defined based on the transformation function and expectation function. Next, three forms of influence matrices incorporating similarity, self-persistence, and authority degrees are constructed, and the consensus conditions of the models are discussed. Then, considering the social networks and incomplete trust relationships between experts, a fourth trust-based influence matrix is devised. A case study of emergency decision-making for assessing response plans to COVID-19 is performed to verify the feasibility and effectiveness of the dynamic method. Furthermore, a sensitivity analysis is conducted. Finally, comparisons with classical methods are performed to illustrate the superiorities of the proposed algorithms.

Multicriteria decision-making methods and application on the basis of probabilistic uncertain trapezium cloud

Probabilistic Uncertain Linguistic Term Set (PULTS), as an emerging and effective linguistic expression tool, can appropriately describe the complex evaluation information of decision makers. The cloud model is powerful in handling complex cognitive linguistic information, based on which, this paper proposes two new Multicriteria Decision-Making (MCDM) Methods with PULTSs. Firstly, to avoid the problem of information loss in traditional linguistic conversion methods, Probabilistic Uncertainty Trapezium Cloud (PUTC) is proposed to quantify linguistic evaluation information. Secondly, the Probabilistic Uncertainty Trapezium Cloud Weighted Bonferroni mean (PUTCWBM) operator is defined, while presenting a new cloud score function and similarity measures. Additionally, two ranking methods are proposed, one on the basis of the similarity measures of PUTCs and ideal solutions, the other on the basis of the PUTCWBM operator and the cloud score function. Finally, the two methods are verified with an example of evaluation on masks, and the effectiveness and superiority of the methods are further illustrated through sensitivity analysis and method comparison.

Multi-attribute decision making method based on probabilistic linguistic term sets and its application in the evaluation of emergency logistics capacity

The occurrence of public health emergency will cause huge economic losses and casualties, which posed a huge threat to the economic and social development. In response to the emergency, a large amount of emergency relief supplies will be transported to the affected areas. Faced with this public health emergency of international concern, the concept of emergency logistics capacity and the evaluation model based on probabilistic linguistic term sets are proposed. In this paper, the emergency logistics capability evaluation is transformed into user demand evaluation, and the importance of each index of emergency logistics capability is determined by using Quality Function Deployment (QFD) and prospect theory. Under the probabilistic language information environment, a multi-attribute decision making method is established by using TODIM method. Finally, an example is given to verify the feasibility of the proposed method.

Broken-Edge Decision-Making Strategy for COVID-19 over Air Railway Composite Network

In order to control the spread of the COVID-19 virus, this study proposes an ARCN-SUTS (air railway composite network susceptible-untested-tested-susceptible) model based on the correlation characteristics of the air railway composite network in mainland China. Furthermore, this study also puts forward a broken-edge decision-making strategy for the purpose of making decision about the edge efficiently broken and avoiding the second outbreak of the virus spread to minimize the economic losses for railway and civil aviation companies. Finally, simulation results demonstrate that the proposed strategy can effectively control the spread of the virus with minimal economic losses.

Extended two-dimensional belief function based on divergence measurement

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.