Paper Info
Title
Prescriptive Learning for Air-Cargo Revenue Management
Abstract
We propose 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. The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in an overall loss of potential revenue for the airline. A DQN method using uncertainty bounds from prediction is proposed for decision making under a prescriptive learning framework. Parts of RL-Cargo have been deployed in the production environment of a large commercial airline company. We have validated the benefits of RL-Cargo using a real dataset. More specifically, we have carried out simulations seeded with real data to compare classical Dynamic Programming and Deep Reinforcement Learning techniques on offloading costs and revenue generation. Our results suggest that prescriptive learning which combines prediction with decision-making provides a principled approach for managing the air cargo revenue ecosystem. Furthermore, the proposed approach can be abstracted to many other application domains where decision making needs to be carried out in face of both data and behavioral uncertainty.
Year
DOI
Venue
2020
10.1109/ICDM50108.2020.00055
2020 IEEE International Conference on Data Mining (ICDM)
Keywords
DocType
ISSN
Reinfocement Learning, Air-Cargo, Prescriptive Learning, Revenue Management
Conference
1550-4786
ISBN
Citations 
PageRank 
978-1-7281-8317-6
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Stefano Giovanni Rizzo100.34
Yixian Chen200.34
Linsey Pang311.11
Ji Lucas4964.12
Zoi Kaoudi521518.55
Jorge Ruiz602.70
Sanjay Chawla71372105.09