Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results
Andrzej Janusz, Antoni Jamiołkowski, Michał Okulewicz
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 399–402 (2022)
Abstract. We describe an international data mining competition FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts that was organized in association with the FedCSIS conference series at the KnowledgePit platform. We explain the competition scope and briefly discuss the results obtained by the most successful teams. We also share the most interesting findings of our post-competition research assisted by the BrightBox technology, and describe our own prediction model that was used as the competition baseline. Finally, we show the results of our experiment conducted with the solution ensembling mechanism provided by KnowledgePit. The goal of this experiment was to find a mixture of submitted predictions that is the most accurate estimation of real execution costs of forwarding contracts.
- E.-S. Lee and D.-W. Song, “Knowledge Management in Freight Forwarding as a Logistics Intermediator: Model and Effectiveness,” Knowledge Management Research & Practice, vol. 16, no. 4, pp. 488–497, 2018. [Online]. Available: https://doi.org/10.1080/14778238.2018.1475848
- R. Burkovskis, “Efficiency of Freight Forwarder’s Participation in the Process of Transportation,” Transport, vol. 23, no. 3, pp. 208–213, 2008. [Online]. Available: https://doi.org/10.3846/1648-4142.2008.23. 208-213
- J.-A. Moscoso-López, I. T. Turias, M. Come, J. Ruiz-Aguilar, and M. Cerbán, “Short-Term Forecasting of Intermodal Freight Using ANNs and SVR: Case of the Port of Algeciras Bay,” Transportation Research Procedia, vol. 18, pp. 108–114, 2016. [Online]. Available: https://doi.org/10.1016/j.trpro.2016.12.015
- A. Balster, O. Hansen, H. Friedrich, and A. Ludwig, “An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning,” Business & Information Systems Engineering, vol. 62, no. 5, pp. 403–416, 2020. [Online]. Available: https://doi.org/10.1007/s12599-020-00653-0
- S. Wickramanayake and H. D. Bandara, “Fuel Consumption Prediction of Fleet Vehicles Using Machine Learning: A Comparative Study,” in 2016 Moratuwa Engineering Research Conference, MERCon 2016, 2016, pp. 90–95. [Online]. Available: https://doi.org/10.1109/MERCon.2016.7480121
- M. A. Hamed, M. H. Khafagy, and R. M. Badry, “Fuel Consumption Prediction Model Using Machine Learning,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 11, 2021. [Online]. Available: https://doi.org/10.14569/IJACSA.2021.0121146
- K. Tsolaki, T. Vafeiadis, A. Nizamis, D. Ioannidis, and D. Tzovaras, “Utilizing Machine Learning on Freight Transportation and Logistics Applications: A Review,” ICT Express, 2022. [Online]. Available: https://doi.org/10.1016/j.icte.2022.02.001
- Y. Konishi, S.-i. Mun, Y. Nishiyama, and J. E. Sung, Determinants of Transport Costs for Inter-regional Trade. Research Institute of Economy, Trade and Industry, 2012.
- B. Kordnejad, “Intermodal Transport Cost Model and Intermodal Distribution in Urban Freight,” Procedia – Social and Behavioral Sciences, vol. 125, pp. 358–372, 2014. [Online]. Available: https://doi.org/10.1016/j.sbspro.2014.01.1480
- S. Camisón-Haba and J. A. Clemente, “A Global Model for the Estimation of Transport Costs,” Economic Research – Ekonomska Istraživanja, vol. 33, no. 1, pp. 2075–2100, 2020. [Online]. Available: https://doi.org/10.1080/1331677X.2019.1584044
- S. Nataraj, C. Alvarez, L. Sada, A. Juan, J. Panadero, and C. Bayliss, “Applying Statistical Learning Methods for Forecasting Prices and Enhancing the Probability of Success in Logistics Tenders,” Transportation Research Procedia, vol. 47, pp. 529–536, 2020. [Online]. Available: https://doi.org/10.1016/j.trpro.2020.03.128
- A. Singh, A. Das, U. K. Bera, and G. M. Lee, “Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks,” IEEE Access, vol. 9, pp. 103 497–103 512, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3098657
- A. Janusz and D. Ślęzak, “KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Compe- titions,” in Proceedings of the 2022 Federated Conference on Computer Science and Intelligence Systems, Sofia, Bulgaria, September 4-7, 2022, ser. Annals of Computer Science and Information Systems, M. Ganzha, M. Paprzycki, and D. Ślęzak, Eds., vol. 30, 2022.
- A. Janusz, T. Tajmajer, M. Świechowski, Ł. Grad, J. Puczniewski, and D. Ślęzak, “Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA’18 Data Mining Competition,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, Poznań, Poland, September 9-12, 2018, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 15, 2018, pp. 189–192. [Online]. Available: https://doi.org/10.15439/2018F386
- A. Janusz, M. Przyborowski, P. Biczyk, and D. Ślęzak, “Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit,” in Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS 2020, Sofia, Bulgaria, September 6-9, 2020, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 21, 2020, pp. 77–80. [Online]. Available: https://doi.org/10.15439/2020F159
- D. Ślęzak, M. Grzegorowski, A. Janusz, M. Kozielski, S. H. Nguyen, M. Sikora, S. Stawicki, and Ł. Wróbel, “A Framework for Learning and Embedding Multi-Sensor Forecasting Models into a Decision Support System: A Case Study of Methane Concentration in Coal Mines,” Information Sciences, vol. 451-452, pp. 112–133, 2018. [Online]. Available: https://doi.org/10.1016/j.ins.2018.04.026
- H.-M. Wong, X. Chen, H.-H. Tam, J. Lin, S. Zhang, S. Yan, X. Li, and K.-C. Wong, “Feature Selection and Feature Extraction: Highlights,” in 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ser. ISMSI 2021, New York, NY, USA, 2021, pp. 49–53. [Online]. Available: https://doi.org/10.1145/3461598.3461606
- T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16, New York, NY, USA, 2016, pp. 785–794. [Online]. Available: https://doi.org/10.1145/2939672.2939785
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS’17, Red Hook, NY, USA, 2017, pp. 3149–3157.
- L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: Unbiased Boosting with Categorical Features,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems, ser. NIPS’18, Red Hook, NY, USA, 2018, pp. 6639–6649.