Application of Genetic Algorithm in Container Vessel Stowage Planning with Carbon Tax Considerations

This study developed a method for optimizing stowage planning for container vessels, a crucial aspect of international trade logistics. Over 80% of global trade depends on containerized transportation; thus, effective stowage planning is essential for minimizing transportation costs and enhancing operational efficiency. In the proposed hybrid optimization approach, integer programming is combined with a genetic algorithm to generate optimal stowage plans. The key factors considered in this method include load capacity limits, stacking constraints, and carbon tax regulations. The proposed method involves maximizing space utilization while minimizing logistics costs, with particular emphasis on reducing port dwell times. The findings of this study indicate that the proposed method considerably improves stowage efficiency, substantially decreases container handling operations, and can provide real-time adjustments to accommodate dynamic operational demands. This study offers valuable insights and actionable strategies for optimizing container shipping operations, supporting the industry’s objectives of enhancing the efficiency and reducing the environmental impact of international trade.