@inproceedings{40aea406194f4be4a8259f0a4ed8f359,
title = "Binary Delivery Time Classification and Vehicle's Reallocation Based on Car Variants. SEAT: A Case Study",
abstract = "This note provides a solution to vehicle's compound allocation problem. It has been treated as a classification task employing different Machine Learning (ML) algorithms. It is performed using the known car attributes and the time that vehicles have spent in the compound region, i.e., inventory warehouse, waiting the customer delivery day. Classification results have been assessed with F1 Score and CatBoost has arisen as the best technique, with values larger than 70%. Finally, reallocation strategy has been tested and outcomes exhibit that company's expert performance is equaled or overcame with respect to time distribution.",
keywords = "Anticipatory Shipping, Automotive OEM, Classification, Customer Delivery Time Distribution, F1 Score, Machine Learning, Vehicle Reallocation",
author = "S{\'a}nchez, {Juan Manuel Garc{\'i}a} and Cardona, {Xavier Vilas{\'i}s} and Mart{\'i}n, {Alexandre Lerma}",
note = "Funding Information: This work is partially funded by the Department de Recerca i Universitats of the Gener-alitat de Catalunya under the Industrial Doctorate Grant DI 2019-34. Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.; 24th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
month = oct,
day = "17",
doi = "10.3233/FAIA220329",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "147--150",
editor = "Atia Cortes and Francisco Grimaldo and Tommaso Flaminio",
booktitle = "Artificial Intelligence Research and Development - Proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence",
}