Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Artificial Intelligence | Journal |
Volume | Citations | PageRank |
abs/1905.09130 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Giovanni Rizzo | 1 | 0 | 0.34 |
| Ji Lucas | 2 | 96 | 4.12 |
| Zoi Kaoudi | 3 | 215 | 18.55 |
| Jorge-arnulfo Quiané-ruiz | 4 | 986 | 61.02 |
| Sanjay Chawla | 5 | 1372 | 105.09 |