AI 中文总结
本研究针对建筑劳动力规划的变时域与总需求约束问题，提出CP-RAF模型，经现场数据验证，其在多场景下预测性能优于基准模型，适配建筑劳动力分配需求。
AI 中文摘要
劳动力规划是建筑项目中需反复开展的运营决策，要求对各单项任务的未来劳动力需求作出精准预测。但实际中，各任务的完工日期不同，导致预测时域存在差异；此外，每日预测劳动力需求的总和必须等于预先规定的总劳动力分配量。多数现有基于机器学习（ML）的预测模型假设输出长度固定，且未明确施加总需求约束，无法满足这些运营要求。为解决该问题，本研究提出了约束保持型残差分配预测模型（CP-RAF）。CP-RAF将观测到的劳动力需求时间序列表示为系数向量，检索具有相似时间形态的已完成任务，再通过相似度加权平均估算剩余任务时长内的分配曲线，将各任务预先规定的剩余劳动力需求按估算曲线分配，同时在保留曲线特征的前提下调整预测时域，以此适配变预测时域并保持总需求约束。本研究利用劳动力需求现场数据对CP-RAF进行评估，结果显示，在中长时域固定长度预测场景中，CP-RAF的性能优于8个基准模型，且在变长度预测场景中始终维持较低的预测误差。通过将运营约束直接融入预测流程，该方法为建筑实践中的劳动力分配提供了适配性框架。
英文摘要
Workforce planning is a recurring operational decision during construction projects that requires accurate forecasts of the future workforce demand for individual tasks. However, in practice, tasks have different completion dates, resulting in variable forecast horizons. In addition, the sum of the predicted daily workforce demands must equal the total workforce allocation specified in advance. Most existing machine-learning (ML)-based forecasting models assume fixed-length outputs and do not explicitly impose an aggregate demand constraint, making them unsuitable for these operational requirements. To address this problem, this study proposes constraint-preserving residual allocation forecasting (CP-RAF). The CP-RAF represents an observed workforce demand time series as a coefficient vector and retrieves completed tasks with similar temporal shapes. Then, it estimates the allocation profile over the remaining task duration using similarity-weight averaging. The predefined remaining workforce demand for each task was distributed according to the estimated profile, and the forecast horizon was adjusted while retaining profile characteristics. This procedure accommodates variable forecast horizons while preserving the aggregate demand constraints. CP-RAF was evaluated using workforce demand field data. The results showed that CP-RAF outperformed eight baseline models in medium- and long-horizon fixed-length forecasting and maintained low forecast errors under variable-length forecasting. By directly incorporating operational constraints into the forecasting procedure, the proposed method provides a framework suitable for workforce allocation in construction practices.
