Multi Commodity Flow

Multi-commodity flow (MCF) problems address the efficient distribution of multiple types of goods across a network, aiming to optimize resource utilization and minimize costs. Current research heavily utilizes machine learning, particularly ensemble methods like Random Forests and boosting algorithms, to model and predict freight mode choices, improving accuracy through techniques such as local modeling and feature engineering. These advancements, along with the application of graph neural networks and federated learning, enhance the analysis of complex, geographically distributed networks like food supply chains, improving resilience assessments and informing resource allocation strategies. The broader impact spans logistics optimization, network infrastructure planning, and the development of more robust and resilient systems.