Enhancing Transportation Efficiency with Optimal Container Placement Using the Bat Algorithm

Abstract

The objective of this article is to provide an in-depth exploration of the complex task of container storage at seaports, a problem characterized as one of the challenging NP (Non-Deterministic Polynomial time) problems. Seaports are faced with the dilemma of accommodating a finite number of containers due to the constrained surface area available, making the management of container storage operations a formidable task. To address this challenge, the present study leverages a meta-heuristic approach aimed at identifying an optimal storage plan for containers within a storage area. This approach is informed by insights drawn from bat swarm intelligence, commonly known as the Bat Algorithm. By integrating principles from this nature-inspired algorithm, the authors seek to develop a robust solution for optimizing container storage strategies in seaports. This approach takes into account several critical constraints, including container travel distances and considerations related to container type and departure dates.