Prescriptive learning for air-cargo revenue management

Stefano Giovanni Rizzo, Yixian Chen, Linsey Pang, Ji Lucas, Zoi Kaoudi, Jorge Quiane, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages462-471
Number of pages10
ISBN (Electronic)9781728183169
DOIs
StatePublished - Nov 2020
Externally publishedYes
Event20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: Nov 17 2020Nov 20 2020

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2020-November
ISSN (Print)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
Country/TerritoryItaly
CityVirtual, Sorrento
Period11/17/2011/20/20

Keywords

  • Reinfocement Learning, Air-Cargo, Prescriptive Learning, Revenue Management

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