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<p>In talent assessment and development decision-making, a holistic perspective on long-term performance is essential. While periodic evaluation data reflects current individual performance, assessment trends reveal patterns of improvement or decline. Integrating these dimensions enables decision-makers to gain comprehensive insights, facilitating choices that balance long-term benefits for both individuals and organizations. In traditional decision-making models, intuitionistic fuzzy sets play a crucial role. Existing improved fuzzy sets, such as intuitionistic fuzzy soft sets, and intuitionistic hesitant fuzzy sets, fail to take into account the multi-stage assessment information and the trend of evaluation information changing over time. To more comprehensively describe evaluation information and obtain more reasonable decision-making results, we propose a new intuitionistic fuzzy set, namely time-series intuitionistic hesitant fuzzy sets (TSIHFSs), and define its basic operators, score function, and distance measures. To simultaneously capture both the numerical differences and the temporal trend differences in membership or non-membership degrees between TSIHFSs, we introduce the Sobolev distance for TSIHFSs. Subsequently, we construct a multi-stage multi-attribute decision-making model by comprehensively utilizing TSIHFSs, their score function, the Sobolev distance, and the VIKOR method.The model was applied to address talent assessment and development issues, such as company intern selection, thereby further validating its effectiveness.</p>

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A multi-stage multi-attribute decision-making model based on the Sobolev distance of time-series intuitionistic hesitant fuzzy sets and application in talent assessment and development

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Juan Liu,
  • Wangyong Lv,
  • Qian Song

摘要

In talent assessment and development decision-making, a holistic perspective on long-term performance is essential. While periodic evaluation data reflects current individual performance, assessment trends reveal patterns of improvement or decline. Integrating these dimensions enables decision-makers to gain comprehensive insights, facilitating choices that balance long-term benefits for both individuals and organizations. In traditional decision-making models, intuitionistic fuzzy sets play a crucial role. Existing improved fuzzy sets, such as intuitionistic fuzzy soft sets, and intuitionistic hesitant fuzzy sets, fail to take into account the multi-stage assessment information and the trend of evaluation information changing over time. To more comprehensively describe evaluation information and obtain more reasonable decision-making results, we propose a new intuitionistic fuzzy set, namely time-series intuitionistic hesitant fuzzy sets (TSIHFSs), and define its basic operators, score function, and distance measures. To simultaneously capture both the numerical differences and the temporal trend differences in membership or non-membership degrees between TSIHFSs, we introduce the Sobolev distance for TSIHFSs. Subsequently, we construct a multi-stage multi-attribute decision-making model by comprehensively utilizing TSIHFSs, their score function, the Sobolev distance, and the VIKOR method.The model was applied to address talent assessment and development issues, such as company intern selection, thereby further validating its effectiveness.