Decision support system based on AHP and PROMETHEE under rough pythagorean fuzzy set information for selection of basketball team

基于AHP和PROMETHEE的决策支持系统,利用粗略毕达哥拉斯模糊集信息进行篮球队选拔

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Abstract

The selection of a team in team-based sporting activities and professional situations is a crucial decision that requires an optimal choice from multiple conditions. The solutions currently available do not simplify the issues of the best team selection under different alternatives. In the modern age, the theory of multi-attribute decision-making (MADM) is a well-known approach for assessing decision-making problems. The main objective of this article is to introducing new aggregation operator (AO) called rough Pythagorean Fuzzy Dombi weighted averaging (RPyFDWA) operator, analytic hierarchy process (AHP) for measuring the weights of alternatives and also investigated the organizing preference rankings for enrichment evaluation (PROMETHEE) for ranking of alternatives by imposing a lower and upper approximations, under rough Pythagorean fuzzy set (RPyFS) framework. The RPyFS framework has a superior structure for handling uncertain data compared to existing frameworks. We also establish significant mathematical characteristics of the operator, including idempotency, monotonicity, and boundedness, to determine its flexibility. We have provided a case study based on a basketball team. The proposed theory is applied to the solution of a non-theoretical example related to assessing team selection issues. We can use our proposed AOs to select the best team from the list of four teams based on various attributes like physical fitness, performance criteria, experience, age, and injury prevention. We notice that the team It is the best team among all other considered teams using the proposed RPyFDWA operator and PROMETHEE method. We provided a comparison between other current approaches and established AOs for analyzing the authenticity and validity of the proposed approach. Also, provide a sensitivity analysis of the proposed study to observe the change in input variables that affects the model's or decision's dependability. A solid conclusion is provided at the end.

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