Optimizing Global Logistics through Hybrid AI. A deep dive into container relocation efficiency using advanced heuristic modeling.
Conventional logistics software often struggles with the non-linear dynamics of maritime traffic. This project leverages K-Means clustering to group container attributes into logical clusters based on weight, destination, and priority.
To manage the inherent uncertainty in operational data, a Fuzzy Inference System (FIS) was integrated, creating a decision layer that mirrors human expert heuristic reasoning.
Key Highlight
"Unsupervised Learning meets Fuzzy Logic"
Defining architectural structure through multi-dimensional attribute grouping.
Navigating ambiguity with linguistic variables and fuzzy rulesets.
Grouping containers based on weight, destination, and priority to minimize relocation overhead and stack reshuffling.
Using fuzzy rules to determine relocation necessity, balancing cost-to-move vs. predicted future efficiency gains.
Managing non-linear relationships in logistics data to ensure robust performance during peak port congestion.
Dive into the raw implementation. Access the full Google Colab notebook to explore the hybrid architecture, fuzzy sets, and clustering analysis.