This study addresses the scheduling optimization problem in curved block workshops during shipbuilding processes by proposing an improved Particle Swarm Optimization (PSO) algorithm integrated with a simulated annealing mechanism (HPSO-SA). Traditional PSO algorithms are prone to falling into local optima and exhibit insufficient convergence efficiency in complex scheduling scenarios. To enhance global search capability and robustness, this study introduces a dynamic inertia weight adjustment strategy and a neighborhood perturbation mechanism derived from simulated annealing. Additionally, a multi-constrained scheduling model is constructed, incorporating the cumulative fatigue effects of workers, with the optimization objective of minimizing the maximum completion time to achieve collaborative optimization of resource allocation and process sequencing. Case analysis using actual production data from a shipyard demonstrates that the proposed method reduces the maximum completion time and improves convergence