Ship Spare Parts Optimization Model Based on Ideal Point Method and Improved Ant Colony Algorithm

This study proposes an optimization approach for ship spare parts allocation by integrating the ideal point method (IPM) with an improved ant colony algorithm. The traditional R/C (Reliability–Cost Ratio) model is constrained by single-objective formulations that fail to reconcile cost efficiency with system reliability, often producing the paradoxical result that fewer spare parts correspond to higher reliability. To address this limitation, a multi-objective model was constructed for reliability–cost optimization, while the enhanced ant colony algorithm identifies optimal spare part configurations that achieve balanced trade-offs. Nine representative scenarios were analyzed, with simulation outcomes compared between the IPM and R/C model approaches. Sensitivity analyses of critical parameters were conducted, and the effectiveness of both approaches was evaluated. The results demonstrate that the IPM consistently achieves higher reliability, particularly under stringent reliability requirements and tighter spare parts constraints. The findings provide a robust analytical foundation for evidence-based decision-making in ship equipment support.