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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yazarlar

İrem Kalafat Bu kişi benim
Türkiye

Mustafa Hekimoğlu Bu kişi benim
Türkiye

Ahmet Deniz Yücekaya Bu kişi benim
Türkiye

Nilay Ay Bu kişi benim
Türkiye

Habib Gültekin Bu kişi benim
Türkiye

Yayımlanma Tarihi

21 Aralık 2021

Gönderilme Tarihi

19 Temmuz 2021

Kabul Tarihi

5 Aralık 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 4 Sayı: 2

IZ

https://izlik.org/JA97FD22HD

APA

Kalafat, İ., Hekimoğlu, M., Yücekaya, A. D., Ay, N., & Gültekin, H. (2021). Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost. Data Science and Applications, 4(2), 19-24. https://izlik.org/JA97FD22HD

AMA

1.Kalafat İ, Hekimoğlu M, Yücekaya AD, Ay N, Gültekin H. Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost. DataSCI. 2021;4(2):19-24. https://izlik.org/JA97FD22HD

Chicago

Kalafat, İrem, Mustafa Hekimoğlu, Ahmet Deniz Yücekaya, Nilay Ay, ve Habib Gültekin. 2021. “Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost”. Data Science and Applications 4 (2): 19-24. https://izlik.org/JA97FD22HD.

EndNote

Kalafat İ, Hekimoğlu M, Yücekaya AD, Ay N, Gültekin H (01 Aralık 2021) Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost. Data Science and Applications 4 2 19–24.

IEEE

[1]İ. Kalafat, M. Hekimoğlu, A. D. Yücekaya, N. Ay, ve H. Gültekin, “Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost”, DataSCI, c. 4, sy 2, ss. 19–24, Ara. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA97FD22HD

ISNAD

Kalafat, İrem - Hekimoğlu, Mustafa - Yücekaya, Ahmet Deniz - Ay, Nilay - Gültekin, Habib. “Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost”. Data Science and Applications 4/2 (01 Aralık 2021): 19-24. https://izlik.org/JA97FD22HD.

JAMA

1.Kalafat İ, Hekimoğlu M, Yücekaya AD, Ay N, Gültekin H. Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost. DataSCI. 2021;4:19–24.

MLA

Kalafat, İrem, vd. “Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost”. Data Science and Applications, c. 4, sy 2, Aralık 2021, ss. 19-24, https://izlik.org/JA97FD22HD.

Vancouver

1.İrem Kalafat, Mustafa Hekimoğlu, Ahmet Deniz Yücekaya, Nilay Ay, Habib Gültekin. Workload Forecasting of Warehouse Stations: Comparison Between Classical Time Series Methods and XGBoost. DataSCI [Internet]. 01 Aralık 2021;4(2):19-24. Erişim adresi: https://izlik.org/JA97FD22HD