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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Key Factors to Consider when Predicting the Costs of Forwarding Contracts

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DOI: http://dx.doi.org/10.15439/2022F293

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 447–450 ()

Full text

Abstract. Predicting the cost of forwarding contracts is atypical problem that logistics companies need to solve in order to optimize their business for a better profit. This is the challenge defined in the FedCSIS 2022 Competition where a five-year history of contract data and their delivery routes from a large Polish logistics company are provided to train a Machine Learning model. In addition to the contract data, historical wholesale fuel prices and euro exchange rates at the contract time are also provided. To address this challenge, we first designed a basic solution where we focused on feature engineering to find good impact features for the model. After that, the same set of features were used to train two different models: one using XGBoost and the other using LightGBM. The average predictions of the two boosting models were then used as the predictions for the next post-processing step. Finally, in the post-processing step, we designed and trained a simple linear regression model to capture the average monthly changes of the contract cost, given the changes of the fuel prices and euro exchange rates. These captured changes were used to post-process (adjust) the predictions in the previous step to address the issue that tree-based models could not predict the value that they did not see before. While the basic solution with careful feature selection gave us a place in the top-5, our post-processing strategy in the last step helped us win the 3 rd prize in the competition.

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