Abstract
The poverty alleviation problem as one of the social evaluation applications has long been a major focus of 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 for some indicators because of the hesitation among different possible valuations in the mind. Different from traditional algorithms only using a single indicator valuation, the paper uses Pythagorean fuzzy sets (PFSs) to keep possible valuations from the positive and negative aspects and it can overcome the hesitation in the data evaluation process to a certain extent. 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 been obtained through the algorithm.
Highlights
With the rapid development of China’s economy, the number of poor people is gradually decreasing
Breedveld (2019) et al introduced the latest multi-criteria decision making algorithms into the medical field, subsequently, several experiments were carried out and the results show that the algorithms can effectively assist doctors in making complex decisions [13]
The VIKOR algorithm can rank alternatives in limited time and can both consider the effect of the group utility and the individual regret
Summary
With the rapid development of China’s economy, the number of poor people is gradually decreasing. As the basis and starting step of the precise poverty alleviation project, it is crucial to accurately identify the targets of poverty alleviation. Based on the survey data in Gansu Province of China, Li (2015) established a logistic model for identifying the poverty alleviation targets, and he pointed out that the health status of family members and the number of minor children had a significant impact on the poverty level of the families. We found that many experts often cannot give a single valuation to express the membership degree in the actual situation; they may hesitate among a series of valuations because of uncertainty in their minds Based on these considerations, Torra(2010) advanced the definition of the hesitant fuzzy sets, which is an extension of the fuzzy sets, it allows the membership degree of each element can have several possible valuations. Shakeel(2019) et al investigated the interval-valued Pythagorean trapezoidal fuzzy aggregation methods and defined some Einstein operational laws [6]
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