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<p>Workload is a critical consideration in the human factors evaluation of nuclear power plants (NPPs), as it significantly influences operator behavior and judgment in the main control room. Operator workload is typically affected by multiple performance-shaping factors (PSFs). However, existing workload prediction methods mainly rely on mission protocol analysis and fail to fully account for the impact of potential PSFs under different operational conditions. In real-world scenarios, demanding PSFs may either increase workload demands or reduce the operator’s workload threshold, negatively affecting performance and increasing operational risk. To address these limitations, this study proposes a workload prediction method that incorporates PSF influences. The method involves: (1) conducting quantitative and qualitative analyses of complex PSF interactions using a weighted influence non-linear gauge system (WINGS) to construct an influence relationship model; (2) deriving a mathematical relationship between human error probability (HEP) and workload. By integrating the influence relationship among PSFs, the method adjusts PSF multipliers to quantify their impact on workload demands or thresholds during visual–auditory–cognitive–psychomotor (VACP) activities. Case validation demonstrates that the proposed method provides more accurate workload prediction than the conventional VACP method. Hence, the proposed workload prediction method enables more accurate assessment of operator workload under varying operational conditions, contributing to safer, more reliable NPP operations.</p>

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A predictive method for operator workload in nuclear power plant main control rooms considering performance-shaping factors

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Shengyuan Yan,
  • Xin Liu,
  • Xiaodan Zhang

摘要

Workload is a critical consideration in the human factors evaluation of nuclear power plants (NPPs), as it significantly influences operator behavior and judgment in the main control room. Operator workload is typically affected by multiple performance-shaping factors (PSFs). However, existing workload prediction methods mainly rely on mission protocol analysis and fail to fully account for the impact of potential PSFs under different operational conditions. In real-world scenarios, demanding PSFs may either increase workload demands or reduce the operator’s workload threshold, negatively affecting performance and increasing operational risk. To address these limitations, this study proposes a workload prediction method that incorporates PSF influences. The method involves: (1) conducting quantitative and qualitative analyses of complex PSF interactions using a weighted influence non-linear gauge system (WINGS) to construct an influence relationship model; (2) deriving a mathematical relationship between human error probability (HEP) and workload. By integrating the influence relationship among PSFs, the method adjusts PSF multipliers to quantify their impact on workload demands or thresholds during visual–auditory–cognitive–psychomotor (VACP) activities. Case validation demonstrates that the proposed method provides more accurate workload prediction than the conventional VACP method. Hence, the proposed workload prediction method enables more accurate assessment of operator workload under varying operational conditions, contributing to safer, more reliable NPP operations.