Spatial Conflict-Aware Multi-Objective Scheduling for Parallel Construction Tasks with Coupled Fatigue Dynamics

Parallel operations by multiple trades on constrained workfaces pit safety against schedule. Static schedulers, scalar fatigue models, and standard evolutionary algorithms all struggle to reconcile the two. Simulation tackles the Parallel Task Matching Problem (PTMP). A Spatial Conflict Graph quantifies workspace interference, with edge weights combining a 3D Jaccard overlap and a process-coupling coefficient. The Cross-Task Fatigue Transfer Model (CTFTM) governs whole-body, localized, and cognitive fatigue. Task-specific accumulation, recovery, and crosstalk parameters in this coupled ODE system capture fatigue carryover across heterogeneous activities. Minimizing schedule delay and OHS risk then follows from a bi-objective model under spatial-conflict, fatigue, skill, crew-size, and precedence constraints. An NSGA-III extension uses integer worker–task encoding, a Conflict Repair Operator that modifies fewer than 7% of genes and removes penalty calibration, and TOPSIS-based Pareto selection. Hybrid re-scheduling pairs persistent event triggers with structural state updates and a periodic trigger. The test campaign used three parallel tasks, eight heterogeneous workers, five ablative baselines, four empirical sensitivity analyses, and a design-level threshold assessment. Standalone calibration produced comparable, trade-off-dependent multi-objective performance for NSGA-III, NSGA-II, and MOEA/D; NSGA-III with conflict repair was retained for the full dynamic experiments. Over-threshold-fatigue workers fell from 5.789 to 0.020 relative to the scalar-fatigue baseline. Fatigue-efficiency index (FEI) improved by 43.5% relative to the scalar-fatigue baseline, alongside robust real-time disruption handling. Taken together, these results establish a simulation-based foundation for intelligent, safety-aware workforce scheduling.