Delivery Time Prediction Using Support Vector Machine Combined withLook-back Approach
DOI:
https://doi.org/10.5281/zenodo.8070704Keywords:
Delivery time prediction, Look-back approach, Machine learning, Support Vector MachineAbstract
Delivery time refers to the time elapsed from the moment the order is created to the delivery of the product to
the final consumer. It is extremely important to predict the delivery time in order to ensure customer
satisfaction and smooth logistics processes. The aim of this study is to predict the delivery time using Support
Vector Machine (SVM) with and without consecutive look-back and periodic look-back approaches. A sample
dataset obtained from Kaggle was used. Mean Absolute Error (MAE) was utilized to evaluate the performance
of the prediction models. According to the results, the average MAE obtained with the look-back approach
(3.81) was 59.12% lower than that obtained without the look-back approach (9.32).
References
N. Salari, S. Liu, Z. J. M. Shen., "Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?", Manufacturing & Service Operations Management Sep., 2022.
M. Lochbrunner, H. F. Witschel. "Combining Machine Learning with Human Knowledge for Delivery Time Estimations.", FHNW - University of Applied Sciences Northwestern Switzerland, Olten, 2022.
A. C. de Araujo, A. Etemad, "End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications", IEEE Internet of Things Journal, vol. 8, no. 23, pp. 17043-17056, Dec., 2021.
J. Khiari, C. Olaverri-Monreal, "Boosting Algorithms for Delivery Time Prediction in Transportation Logistics," in 2020 International Conference on Data Mining Workshops ICDMW, pp. 251-258, Sorrento 2020.
M. Abebe, Y. Shin, Y. Noh, S. Lee, I. Lee, “Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping”. Applied Sciences vol. 10, no.7, pp. 2325, March, 2020.
L. Barua, B. Zou, Y. Zhou, “Machine learning for international freight transportation management: A comprehensive review”, Research in Transportation Business & Management, vol. 34, pp. 100453, 2020.
A. Viellechner, S. Spinler, “Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms”, Proceedings of the 53rd Hawaii International Conference on
System Sciences, 2020
N. Servos, X. Liu, M. Teucke, M. Freitag, “Travel time prediction in a multimodal freight transport relation using machine learning algorithms”, Logistics vol. 4, no.1, pp. 1, Dec., 2019.
B. Jia, N. G. M. Rytter, L. B. Reinhardt, G. Haulot, M. B Billesø, “Estimating Discharge Time of Cargo Units–A Case of Ro-Ro Shipping” in International Conference on Computational Logistics pp. 122-135, Barranquilla,
F. Wu, L. Wu. "DeepETA: a spatial-temporal sequential neural network model for estimating time of arrival in package delivery system." in Proceedings of the AAAI Conference on Artificial Intelligence, Hawaii 2019.
https://www.kaggle.com/datasets/salil007/1-shipping-optimization-challenge?select=test_2.csv
A. C. Jonquais, F. Krempl. "Predicting Shipping Time with Machine Learning." 2019.
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