Key Factors to Consider when Predicting the Costs of Forwarding Contracts

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 fiveyear 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 treebased 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.

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

“…We use this competition as an illustration how KnowledgePit works, as well as a prerequisite for the discussion in Section V. We refer to [7] for more details about this particular contest. The best solutions submitted by its participants are described in [23] (1st place), [24] (2nd), [25] (3rd), and [26] (4th).…”

“…V. MIXING THE SOLUTIONS Due to a large number of submitted solutions and the fact that most of the best-performing teams used some advanced ensembling methods to compute their final predictions [23], [24], [25], [26], we decided to check how accurate the predictions could be if we mix submissions from different teams. In this way, not only could we verify which solutions are the most influential and potentially worth deploying, but also we could get a better insight into where the limit to prediction quality is when working with this type of the data.…”

“…Table II shows the results for the obtained ensembles. As a reference, we include the results obtained using simple averaging of the best solutions from top 5 teams (solutions [23], [24], [25], [26] and the baseline model), and the results of weighted averaging in which the weights correspond to the preliminary scores. We also include results obtained for the simple averaging, and weighted averaging when the number of used solutions corresponds to the optimal selection of the λ value.…”

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

“…In case of the external fleet contract pricing, the contracts are priced according to the varying contractor policy, which takes into account several objective and subjective market and non-market factors [1,2], such as contract0dependent, economic, regulatory, general and purely ambivalent factors. Those factors potentially reflect, in the opinion of the decision maker, the shipping cost for a given commodity, along some determined route within a certain time period.…”

“…It is worth noting that the AI and ML methods [7] lead in that area. The literature mostly focuses on the blind machine learning approaches [2,[8][9][10] or hybrid ones [11].…”