<p>Blast-induced rock movement is a key factor leading to discrepancies between pre- and post-blast ore–waste distributions in open-pit mining and resulting in significant ore loss and economic underperformance when not properly addressed. This study proposes and evaluates three dig-limit determination methods—PH-based (practical heuristic), BILP-based (binary integer linear programming), and GA-based (genetic algorithm)—that incorporate blast movement prediction to improve excavation accuracy and economic outcomes. Using field data from two blast blocks at an open-pit uranium mine, post-blast ore–waste distributions were reconstructed based on predicted rock movement, and the three methods were applied to optimize dig-limits accordingly. Comparative analyses revealed that integrating rock movement prediction into dig-limit design can increase profits by 17.3–23.8% relative to manual delineation, and by up to 48.3% when compared to the cases ignoring rock movement. Among the methods, the GA-based approach demonstrated the highest economic benefits, especially when penalty parameters and selective size were properly calibrated. However, the PH-based and BILP-based methods offer competitive performance with lower computational costs, making them suitable for routine applications to low-to-medium-value operations. These findings highlight the critical role of rock movement modeling and optimization algorithms in enhancing ore recovery and economic efficiency in open-pit mining.</p>