Scholarlink

<p>Optimization of personalized sports training programs is a multi-criteria decision-making problem in which conflicting criteria such as improvement in performance, endurance, efficient recovery, risk of injury and use of resources are present under uncertainty. To solve this problem, in this paper, a smart decision-making model is proposed based on multi-criteria decision-making (MCDM), which is a Fermatean fuzzy collaborative unbiased rank list integration (FF-CURLI) type. The proposed model enables data-based analysis and is able to handle uncertainty and uncertainty in expert judgement well. Nine criteria (e.g., benefit type, cost type) are applied to 15 training options by four decision-makers. FF-CURLI aggregation operator is employed in order to combine the expertise and come up with final rankings of training plans. The results will help to identify the most suitable format of trainability for the individual athlete. Comparative and sensitivity analysis with the available Fermatean fuzzy MCDM approaches validate robustness and excellence of the proposed framework. The paper shows that the hybrid combination of fuzzy MCDM techniques is a dependable and efficient decision-support framework to optimize intelligent sports training.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An intelligent decision framework for personalized sports training plan optimization using fermatean fuzzy CURLI based MCDM

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

查看全文
  • Jiuyang Zhang,
  • Xiaotong Zhang,
  • Yisu Yao

摘要

Optimization of personalized sports training programs is a multi-criteria decision-making problem in which conflicting criteria such as improvement in performance, endurance, efficient recovery, risk of injury and use of resources are present under uncertainty. To solve this problem, in this paper, a smart decision-making model is proposed based on multi-criteria decision-making (MCDM), which is a Fermatean fuzzy collaborative unbiased rank list integration (FF-CURLI) type. The proposed model enables data-based analysis and is able to handle uncertainty and uncertainty in expert judgement well. Nine criteria (e.g., benefit type, cost type) are applied to 15 training options by four decision-makers. FF-CURLI aggregation operator is employed in order to combine the expertise and come up with final rankings of training plans. The results will help to identify the most suitable format of trainability for the individual athlete. Comparative and sensitivity analysis with the available Fermatean fuzzy MCDM approaches validate robustness and excellence of the proposed framework. The paper shows that the hybrid combination of fuzzy MCDM techniques is a dependable and efficient decision-support framework to optimize intelligent sports training.