We get asked about federated learning and production scenarios. Here we start with real-world use cases, proven and accepted, in this case via IEEE Xplore.
=> Airfreight revenue learning
RL-Cargo, a revenue management approach for air-cargo that combines machine learning prediction with decision-making using deep reinforcement learning. This approach addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual amount received at departure time by the airline.
Large airlines using this model for optimized freight management, which resulting much better freight plan workloads.
The full article is available via the IEEE explore showcase page:
https://ieeexplore.ieee.org/document/9338388
=> Airfreight revenue learning
RL-Cargo, a revenue management approach for air-cargo that combines machine learning prediction with decision-making using deep reinforcement learning. This approach addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual amount received at departure time by the airline.
Large airlines using this model for optimized freight management, which resulting much better freight plan workloads.
The full article is available via the IEEE explore showcase page:
https://ieeexplore.ieee.org/document/9338388