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We propose AI-CARGO, a revenue management system for air-cargo that combinesmachine learning prediction with decision-making using mathematicaloptimization methods. AI-CARGO addresses a problem that is unique to theair-cargo business, namely the wide discrepancy between the quantity (weight orvolume) that a shipper will book and the actual received amount at departuretime by the airline. The discrepancy results in sub-optimal and inefficientbehavior by both the shipper and the airline resulting in the overall loss ofpotential revenue for the airline. AI-CARGO also includes a data cleaningcomponent to deal with the heterogeneous forms in which booking data istransmitted to the airline cargo system. AI-CARGO is deployed in the productionenvironment of a large commercial airline company. We have validated thebenefits of AI-CARGO using real and synthetic datasets. Especially, we havecarried out simulations using dynamic programming techniques to elicit theimpact on offloading costs and revenue generation of our proposed system. Ourresults suggest that combining prediction within a decision-making frameworkcan help dramatically to reduce offloading costs and optimize revenuegeneration.

AI-CARGO: A Data-Driven Air-Cargo Revenue Management System