Optimizing Space Utilization In Container Packing

Abstract

Container packing presents a complex optimization problem that seeks to efficiently pack diverse items into a fixed-size container. This study provides a comparative analysis of four algorithms -- Greedy Shelf, Shelfing with Rotation, Shelfing with Search, and Guillotine Paper Cutting -- investigating their proficiency in solving the container packing problem. Utilizing a set of four packages with differing dimensions and IDs, The study evaluated the performance of each algorithm. The results demonstrated that the Shelfing with Search algorithm outperformed its counterparts by yielding a stack height of 3019610 L units. Conversely, the Guillotine Paper Cutting algorithm performed poorly, with a stack height of 7537295 units. This research also explored the impact of different sorting methods on packing efficiency, revealing that sorting packages in descending order of height yields superior results. Consequently, this study provides an extensive evaluation of the various algorithms used for container packing, while suggesting promising directions for future research to enhance packing efficiency.