EURO 2025 Leeds
Abstract Submission

1829. Optimizing Empty ULD Repositioning Using Machine Learning and Optimization Techniques

Invited abstract in session MD-23: OR for a Better Africa - OR@Africa 2, stream OR for Societal Development.

Monday, 14:30-16:00
Room: Esther Simpson 3.01

Authors (first author is the speaker)

1. Ali Barooni
Computer Engineering and Software Engineering Department, Polytechnique Montréal
2. Frédéric Quesnel
GERAD
3. Francois Soumis
GERAD
4. Daniel Aloise
Polytechnique Montreal

Abstract

Effective management of Empty Unit Load Devices (ULDs) repositioning is crucial for optimizing efficiency and reducing costs in aviation. Repositioning empty ULDs is expensive and constrained by limited flight capacity, while high demand variance makes distribution challenging. In this study, we tackle imbalanced ULD distribution across airports by developing a data-driven approach to optimize their movement.

Our approach has two stages. First, we forecast ULD demand per flight using features such as aircraft type and route characteristics. We initially applied regression-based models—including XGBoost and a Zero-Inflated Poisson (ZIP) model—to predict ULD requirements. To boost accuracy, we then incorporated time series techniques using Prophet and other methods to capture temporal dependencies in each airport. Second, we built an optimization model to determine the optimal movement of empty ULDs across the network, ensuring cost-effective repositioning while considering operational constraints.

A key contribution of our work is leveraging real-world airline data to enhance the reliability and relevance of our findings. Although similar optimization methods have been used in container shipping, their application in aviation for ULD management is emerging. Our results offer valuable insights for airlines aiming to reduce empty repositioning costs, improve fleet utilization, and minimize unnecessary transfers through data-driven decision-making.

Keywords

Status: accepted

Back to the list of papers