Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost

İrem Kalafat; Mustafa Hekimoğlu; Ahmet Deniz Yücekaya; Nilay Ay; Habib Gültekin

Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost

Abstract

Effective management of warehouse processes is essential in order to maintain high-level service quality and keep the costs at optimum. Each item passes through numerous workstations during their journey in warehouses from the entrepot to the shipping area. Accurate estimation of workload at stations allows personnel assignment optimization and the increase of the warehouse performance. Otherwise, it causes personnel shortages at stations, delays in shipment commitment dates and disruptions in warehouse activities. In this paper, time series forecasting models are used to estimate the load in each workstation for a better operation. The proposed methodologies are applied to an automotive spare part warehouse in Turkey. The classical time series method, which performs best in estimating the workload of each workstation, is presented and these results are compared with the XGBoost model. Thus, the models that give the best results for each station are shown. The proposed research covers part acceptance, storage, order picking and packaging processes and their substations, which were not considered in previous studies.

Keywords

Machine Learning , Warehouse Management , Workload Prediction , Time Series , XGBoost

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Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

İrem Kalafat This is me
Türkiye

Mustafa Hekimoğlu This is me
Türkiye

Ahmet Deniz Yücekaya This is me
Türkiye

Nilay Ay This is me
Türkiye

Habib Gültekin This is me
Türkiye

Publication Date

December 21, 2021

Submission Date

July 19, 2021

Acceptance Date

December 5, 2021

Published in Issue

Year 2021 Volume: 4 Number: 2

IZ

https://izlik.org/JA97FD22HD

IEEE

[1]İ. Kalafat, M. Hekimoğlu, A. D. Yücekaya, N. Ay, and H. Gültekin, “Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost”, DataSCI, vol. 4, no. 2, pp. 19–24, Dec. 2021, [Online]. Available: https://izlik.org/JA97FD22HD