Ant Colony Optimization for Container Lo

The Container Loading Problem (CLP) involves loading a subset of given rectangular boxes into a three-dimensional container of fixed dimensions in the most optimal way, which is crucial in logistics and warehousing to reduce costs by maximizing space utilization. This paper proposes a two-phased approach using Ant Colony Optimization (ACO) to solve the CLP, incorporating a tower building heuristic as the inner heuristic. In the first phase, ACO constructs a sequence of boxes using a probabilistic decision rule. In the second phase, the boxes are arranged into the container with the tower building heuristic, while pheromone feedback from ACO aids in solution improvement through an updating rule. Computational experiments were conducted using benchmark datasets, and the results demonstrate that the proposed algorithm's performance is comparable to other methods documented in the literature. The findings indicate that ACO effectively addresses the CLP, providing a feasible optimization strategy for real-world applications.