Key Factors to Consider when Predicting the Costs of Forwarding Contracts
Quang Hieu Vu, Ling Cen, Dymitr Ruta, Ming Liu
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 (2022)
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.
- A. Janusz, A. Jamiołkowski, M. Okulewicz, "Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results", Proceedings of the 17th Conference on Computer Science and Intelligent Systems (FedCSIS), 2022.
- A. Janusz, M. Przyborowski, P. Biczyk, D. Ślęzak, "Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit", Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS), 2020, http://dx.doi.org/10.15439/2020F159.
- A. Janusz, G. Hao, D. Kaluza, T. Li, R. Wojciechowski, D. Ślęzak, "Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results", IEEE International Conference on Big Data, 2020, http://dx.doi.org/10.1109/BigData50022.2020.9378024.
- D. Ruta, L. Cen, Q. H. Vu, "Deep Bi-Directional LSTM Networks for Device Workload Forecasting", Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS), 2020, http://dx.doi.org/10.15439/2020F213.
- A. Singh, A. Das, U. K. Bera, G. M. Lee, "Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks", IEEE Access, vol. 9, 2021, http://dx.doi.org/10.1109/ACCESS.2021.3098657.
- L. Breiman, “Bagging predictors”, Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
- L. Mason, J. Baxter, P. Bartlett, and M. Frean, "Boosting Algorithms as Gradient Descent", in S.A. Solla, T.K. Leen, and K.R. Muller, editors, Advances in Neural Information Processing Systems 12, MIT Press.
- L. Mason, J. Baxter, P.L. Bartlett, and M. Frean, "Boosting algorithms as gradient descent", Proceedings of International Conference on Neural Information Processing Systems, MIT Press, 1999.
- J. H. Friedman, "Greedy function approximation: A gradient boosting machine", Ann. Statist., vol. 29, no. 5, pp. 1189-1232, 2001.
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T. Liu, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree", Proc. Neural Information Processing Systems Conference (NIPS), 2017.
- U. Gazder and N. T. Ratrout, "A new logit-artificial neural network ensemble for mode choice modeling: A case study for border transport", J. Adv. Transp., vol. 49, no. 8, pp. 855-866, 2015.
- I. C. Bilegan, T. G. Crainic, and M. Gendreau, "Forecasting freight demand at intermodal terminals using neural networks–an integrated framework", Eur. J. Oper. Res, vol. 13, no. 1, pp. 22-36, 2008.
- K. Kumar, M. Parida, and V. K. Katiyar, "Short term traffic flow prediction for a non urban highway using artificial neural network", Procedia - Social and Behavioral Sciences, vol. 104, pp. 755-764, 2013.
- S. Nataraj, C. Alvareza, L. Sadaa, A. Juana, J. Panaderoa, C. Bayliss, "Applying Statistical Learning Methods for Forecasting Prices and Enhancing the Probability of Success in Logistics Tenders", Transportation Research Procedia (Elsevier), vol. 47, 2020.
- S. Lundberg, S. Lee, "A Unified Approach to Interpreting Model Predictions", Advances in Neural Information Processing Systems 30 (NIPS), 2017.
- Q. H. Vu, D. Ruta, L. Cen, M. Liu, "A combination of general and specific models to predict victories in video games", IEEE International Conference on Big Data (Big Data), 2021, http://dx.doi.org/10.1109/BigData52589.2021.9671285.