Hybrid simulation and labor allocation for optimized commercial construction scheduling during COVID-19

Purpose This study develops and validates an integrated simulation framework combining agent-based modeling (ABM), system dynamics (SD) and a modified SEIR epidemiological model to evaluate COVID-19 impacts on commercial construction scheduling and labor allocation. Utilizing empirical data from four construction projects, it examines how varying compliance with safety measures – mask usage, social distancing and vaccination – affects workforce availability, project duration, and costs. The goal is to provide a data-driven decision-support tool for construction managers and policymakers to optimize outcomes and enhance resilience during pandemics. Design/methodology/approach The integrated simulation framework combines ABM, SD and an SEIR model, employing empirical production rates, labor attributes and cost structures from four projects. Workers and tasks are modeled as interactive agents with health status determined by demographic and behavioral factors affecting infection risks. Monte Carlo simulations (n = 1,000) tested varying mask usage, social distancing and vaccination scenarios. Validation involved quantitative metrics (MAPE and RMSE) and expert feedback. Findings Compliance significantly reduced project delays and costs. Noncompliance scenarios increased durations and costs by 9–12%, disproportionately affecting larger sites. High vaccination rates improved workforce stability. Sensitivity analyses highlighted infection probabilities and critical task productivity rates as key performance determinants. Originality/value The study uniquely integrates ABM, SD and epidemiological modeling using real-world data. It offers a robust decision-support framework explicitly addressing the interplay of labor availability, health protocols and construction project outcomes under pandemic-induced uncertainties.