Variable-Horizon Workforce Demand Forecasting with an Aggregate Demand Constraint for Construction Workforce Planning

2026-08-05

AI Summary

This study addresses the challenge of predicting individual task labor demand under variable forecasting horizons caused by heterogeneous task durations in construction projects, where predictions must adhere to a predefined total labor constraint. To this end, the authors propose the Constraint-Preserving Residual Allocation Forecasting (CP-RAF) method, which encodes historical labor sequences into temporal shape coefficient vectors and generates a labor distribution profile for the remaining duration by retrieving and similarity-weighting completed tasks. This profile dynamically allocates the total labor quota and adjusts the prediction horizon accordingly. CP-RAF is the first approach to explicitly embed the total labor constraint directly into the forecasting process, balancing operational feasibility with predictive accuracy. Experimental results on real-world construction site data demonstrate that CP-RAF significantly outperforms eight baseline models, achieving consistently low prediction errors across both medium- and long-term forecasting scenarios with fixed and variable horizons.

Abstract

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.

Problem

• workforce demand forecasting
• variable-horizon forecasting
• aggregate demand constraint
• construction workforce planning
• operational constraints

Innovation

• variable-horizon forecasting
• aggregate demand constraint
• construction workforce planning
• constraint-preserving forecasting
• residual allocation