Abstract
This paper deals with the three-dimensional container loading problem (3D-CLP). Given a set of items known as boxes to be loaded into a rectangular box of larger dimensions known as a container. The aim is to maximize the space utilization and the value of the items packed in the container while adhering to fundamental constraints such as overlapping and overstepping items, orientation, and weight limits. The 3D-CLP is NP-Hard that is, finding the best solution requires exponential time. Many scholars have adopt the use of metaheuristic approaches in recent times. Therefore, this paper presents a hybrid multi-objective optimization approach, a non-dominated sorting Genetic Algorithm-II (NSGA-II) to address the 3D-CLP problem. In addition, we utilize the single-deep bottom left fill approach as a packing heuristic to load items into the container. The experiments demonstrate the practical effectiveness of the proposed approach. This highlights the efficiency of the approach and demonstrates its potential relevance in real-world supply chain operations.