  We propose AI-CARGO, a revenue management system for air-cargo that combines
machine learning prediction with decision-making using mathematical
optimization methods. AI-CARGO 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 received amount at departure
time by the airline. The discrepancy results in sub-optimal and inefficient
behavior by both the shipper and the airline resulting in the overall loss of
potential revenue for the airline. AI-CARGO also includes a data cleaning
component to deal with the heterogeneous forms in which booking data is
transmitted to the airline cargo system. AI-CARGO is deployed in the production
environment of a large commercial airline company. We have validated the
benefits of AI-CARGO using real and synthetic datasets. Especially, we have
carried out simulations using dynamic programming techniques to elicit the
impact on offloading costs and revenue generation of our proposed system. Our
results suggest that combining prediction within a decision-making framework
can help dramatically to reduce offloading costs and optimize revenue
generation.
