Gists
Abstract
Gemini 2.5 Pro Experimental enabled automated cargo ship stowage planning via prompt engineering, overcoming prior model limitations. This eliminates the need for complex algorithms, demonstrating AI’s potential in logistics.
Introduction
Recently, I encountered a practical business challenge: automating stowage planning through AI. Specifically, I received a request to generate optimal container loading plans for cargo ships, a task traditionally requiring significant manual effort and domain expertise. In initial tests, prior to the release of Gemini 2.5, I found that existing models struggled to effectively handle the complexities of this problem, including constraints like weight distribution, container dimensions, and destination sequencing. However, with the release of Gemini 2.5, I observed a significant improvement in the model’s capabilities. Utilizing the Gemini 2.5 Pro Experimental model, I successfully demonstrated the generation of viable stowage plans using only carefully crafted prompts. This breakthrough eliminates the need for complex, custom-built algorithms or extensive training datasets. The successful implementation involved providing the model with key parameters such as container dimensions, weights, destination ports, and ship capacity. This report details the methodology, prompt engineering, and results of my attempt to create automated stowage planning using Gemini 2.5 Pro Experimental, highlighting its potential to revolutionize logistics and shipping operations.