Comparative Analysis of Algorithms for Cargo Container Space Optimization

This work delves into optimizing container space utilization in contemporary logistics and warehousing by addressing the intricate challenge of efficiently fitting various tire types into 2D and 3D containers. In our work we have used cutting-edge algorithms such as Next Fit, Best Fit Decreasing, Genetic Algorithm, Dynamic Programming, and Ant Colony Optimization, the study aims to maximize container space utilization while offering insights into tire arrangement and container occupancy percentages. Our evaluation showed significant differences in algorithm performance regarding time and space complexity. Next Fit is simple but can lead to suboptimal space usage. Best Fit Decreasing optimizes space better but demands more computation. Genetic Algorithm and Dynamic Programming are competitive but resource-intensive. Ant Colony Optimization excels in container space optimization with a unique, nature-inspired approach. This study serves as a valuable reference for professionals and researchers tackling complex packing challenges in logistics and warehousing, fostering advancements in efficiency.