Key Factors to Consider when Predicting the Costs of Forwarding Contracts

Quang Hieu Vu, Ling Cen, Dymitr Ruta, Ming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Predicting the cost of forwarding contracts is a typical 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 3rd prize in the competition.

Original languageBritish English
Title of host publicationProceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022
EditorsMaria Ganzha, Leszek Maciaszek, Leszek Maciaszek, Marcin Paprzycki, Dominik Slezak
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages447-450
Number of pages4
ISBN (Electronic)9788396589712
DOIs
StatePublished - 2022
Event17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022 - Sofia, Bulgaria
Duration: 4 Sep 20227 Sep 2022

Publication series

NameProceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022

Conference

Conference17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022
Country/TerritoryBulgaria
CitySofia
Period4/09/227/09/22

Keywords

  • Feature Engineering
  • Forwarding Contract Cost Prediction
  • Gradient Boosting Trees
  • LightGBM
  • Linear Regression
  • Logistics
  • Post-Processing
  • XGBoost

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