The poverty alleviation problem as one of the social evaluation applications has long been a major focus of the social problems. As the basis and starting step of the poverty alleviation project, it is crucial to accurately identify the targets of poverty alleviation. Therefore, first of all, it is necessary to establish a scientific and reasonable indicators system and then evaluate all the indicator values respectively. However, in the process of data evaluation, we found that it is often hard to decide the unique valuation of some indicators because of the hesitation among different possible valuations. Different from the traditional algorithms using a single indicator valuation, the paper uses Pythagorean fuzzy sets (PFSs) to keep possible valuations from the positive and negative aspects to overcome the hesitation in the data evaluation process. The paper considers the problem of identifying the poverty alleviation targets as a multi-criteria decision making (MCDM) problem and then proposes a modified algorithm to solve the problem on the basis of several traditional algorithms. The algorithm work well and can obtain the maximum group utility and the minimum individual regret at the same time in the following experiments. The optimal poverty alleviation targets have been found and the poverty ranking list has also obtained through the algorithm. INDEX TERMS Pythagorean fuzzy sets, Multi-criteria decision making problem, Precise poverty alleviation, Fuzzy decision making.

At present, the whole world is facing the serious challenge of COVID-19, and it has reached a consensus that taking appropriate measures timely is the key to prevent and control infectious diseases. This paper proposes an algorithm to solve the problem of how to choose the most appropriate alternative from numerous alternatives in the limited time from the perspective of management. First of all, we have compared various data structures for keeping the comparison results of alternatives. After comparisons, we adopt the hesitant fuzzy incomplete probabilistic linguistic preference relation matrix to save the information which can keep the first-hand valuable collected data to the maximum extent; then, we can obtain the missing values with the help of the fault tree analysis method, which can consider both subjective evaluation data and objective historical data simultaneously. Meanwhile, the fault tree analysis method can find development laws with the help of similar infectious diseases that have occurred in the past. The definition of consistency index is also introduced which can measure whether there are contradictions and the degree of contradiction in the decision results. Only those data that meet the consistency requirements can be used for decision-making and then a method is proposed to effectively reduce the degree of inconsistency. The information aggregation method will be adopted subsequently, and we can obtain the ranking of alternatives. An instance with specific execution steps is also introduced to illustrate the feasibility and efficiency of the algorithm proposed in this paper; in the end, several types of comparisons with typical algorithms proposed by other scholars are carried out, and all the experimental results show that the algorithm proposed in this paper is effective and innovative in some aspects.

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences.

From trivial matters in life to major scientific projects related to the fate of mankind, decision-making is everywhere. Whether high-quality decisions can be made often directly affects the development of affairs, especially when sudden disasters occur. As the basis of decision-making, data are crucial. The continuously probabilistic linguistic set, a data structure of the fuzzy mathematics, is selected in the paper to collect original data after careful comparisons, because this data structure can fully consider the hesitation of decision-makers and the fuzziness of complex problems. Although all alternatives are costly, the costs of different alternatives still vary greatly; obviously, the low-cost alternative is better than others when the same predetermined goal can be achieved, which is one of the research objectives and characteristics of this paper. Different from other researchers who only take the cost as one of the decision-making indicators, the algorithm proposed in the paper pays much more attention on the cost reduction. When dealing with an emergency, it is often difficult to solve the problem by taking measures only once; usually, multiple rounds of measures are needed. Each round of decision-making has both connections and differences, and the multiround decision-making model is proposed and built in the paper. Different from traditional linear structures, the model mainly adopts the closed-loop structure, which divides the whole process into multiple sub-decision-making points, the severities measured at the current time point will be compared with the values estimated at the latter time point, and then, the differences will be input into the system, the corresponding automatic adjustment modules will be activated immediately according to the values. The accuracy of the system can be verified and adjusted in time by the closed-loop control module. Finally, several experiments are carried out and the results show that the algorithm proposed in the paper is more effective and the cost is lower.

The innovation ability of students is one of the most important objectives that need to be cultivated in colleges and universities. The comprehensive evaluation of innovation ability discussed in the study can be divided into two stages: the first stage can be called preliminary evaluation and its main target is to identify students with innovative potential; in the second stage, the target objects found in the previous stage will be evaluated quantitatively and ranked. However, it is always difficult to quantitatively evaluate the innovation ability by using traditional algorithms. Based on the above analysis, the study proposes an algorithm to quantitatively evaluate the innovation ability with the help of management thought and fuzzy mathematics. Data are the basis of evaluation, and the accuracy of the data directly determines the quality of the algorithm; the data structure of the incompletely probabilistic fuzzy set is proposed in the study; the data structure can fully consider the fuzziness of the problem and the hesitation in the decision-making process; it can save the original detailed data to the maximum extent. Certainly, certain information may be lost or only the value range can be determined; there are usually some unknowns in the evaluation data, and the consistency optimization model is proposed for solving the problem. Usually, there are certain contradictions among the evaluation data; the definition of the consistency degree is proposed in the study; the consistency can be verified in time after all the unknowns are obtained, and the automatic adjustment module will be activated immediately if the value of the consistency degree exceeds the warning threshold. Finally, after verifying the data consistency, the solution can be obtained by solving the optimization model. Several experiments have been carried out to verify the effectiveness and high-discrimination ability of the algorithm proposed in the study; meanwhile, the superiority of the algorithm is further verified through comparisons with other outstanding algorithms.