TipPri has launched an innovative project, GraphILO (Graph Neural Networks for Internal Logistics Optimization), aimed at improving internal logistics in manufacturing. By leveraging graph neural networks(GNNs), GraphILO optimizes job scheduling and enhances efficiency in industrial processes, from warehousing to production flow management.
Traditional optimization methods, such as genetic algorithms and classical machine learning, struggle with the complexity of real-world logistics systems. GraphILO introduces a novel approach by structuring logistics tasks as a dynamic graph and applying GNN-based deep learning techniques. This allows manufacturers to optimize scheduling, minimize delays, and improve overall equipment utilization.
GraphILO is designed for seamless integration with ERP/WMS systems, making it highly adaptable for industrial environments. A key aspect of the project is its collaboration with a local manufacturing plant, where real-time data is used for model validation and optimization. This ensures the solution is both effective and user-friendly.
With projected efficiency improvements of up to 20% over traditional methods, GraphILO offers a high return on investment and supports sustainable manufacturing. By reducing energy consumption and waste through smarter scheduling, it directly contributes to environmental sustainability goals.
Through this initiative, TipPri is positioning itself at the forefront of smart factory optimization, bringing cutting-edge AI solutions to the industrial sector and paving the way for future advancements in logistics automation.
