Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts

Abstract

A common business practice for transportation forwarders is to bid for shipping contracts at the transport or freight exchanges. Based on the detailed contract requirements they try to estimate the total expected cost of its execution and accordingly bid with the fixed price in advance for delivering such shipping service at the prescribed specification and schedule. The capability to accurately predict the cost of contract execution is the critical factor deciding about the profitability of offered shipping services as well as the amount of business drawn from freight exchanges. However, given highly volatile nature of the transport services ecosystem, it is difficult to simultaneously account for countless dynamically changing factors like fuel prices, currency exchange rates, temporal and spatial multitude of routing and implied traffic risks, the properties of cargo and shipping vehicles etc., which leads to big cost under- or overestimation resulting with loss-making contracts or equally painful missed revenue opportunities. In the context of FedCSIS 2022 data mining competition we propose an accurate and robust predictor of the cost of forwarding contracts built upon the detailed contract data using the ensemble of the state-of-the-art gradient boosting-based regression models. Our established feature engineering framework combined with deep parametric optimization of the individual models and multi-faceted diversification techniques guiding hybrid final model ensembles were instrumental to outperform all the competitive predictors and win the FedCSIS 2022 contest.