Commodity demand forecasting using modulated rank reduction for humanitarian logistics planning

Abstract

Demand prediction for humanitarian logistics is a complex problem with immediate real-world consequences. This paper examines fuel demand during two regional humanitarian crisis events and the supply chain operated by the US Government as part of Operation Unified Response. Because typical machine learning algorithms require large amounts of training data, our methods for predictive analysis depend on rapid training of a model where re-sampling would not be useful due to dynamic time-series data. We propose an online robust principal components analysis (RPCA) model combined with a long short-term memory (LSTM) recurrent network to address this challenge. Our computational results demonstrate that the proposed model can predict demand efficiently on real-world humanitarian supply datasets and well-known benchmark datasets in the University of California, Irvine (UCI) Machine Learning Repository. This method also allows us to tune training lag in online learning.