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Application of Machine Learning Methods for Pallet Loading Problem

Department of Industrial Engineering, Turkish-German University, Şahinkaya Caddesi 106, Beykoz, Istanbul 34820, Turkey
Vocational School of Technical Science, Isparta University of Applied Sciences, Isparta 32260, Turkey
Department of Mechatronics Engineering, Akdeniz University, Antalya 07058, Turkey
Stocker Center 283, Industrial and Systems Engineering, Ohio University, Athens, OH 45701, USA
Department of Industrial Systems Engineering, Mutah University, Alkarak 61710, Jordan
DHL Supply Chain, Solutions Design, Westerville, OH 43082, USA
Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Author to whom correspondence should be addressed.
Academic Editor: Luis Javier García Villalba
Appl. Sci. 2021, 11(18), 8304;
Received: 29 July 2021 / Revised: 31 August 2021 / Accepted: 2 September 2021 / Published: 7 September 2021
(This article belongs to the Topic Machine and Deep Learning)
Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology. View Full-Text
Keywords: logistics; machine learning; pallet loading problem (PLP); classifiers logistics; machine learning; pallet loading problem (PLP); classifiers
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MDPI and ACS Style

Aylak, B.L.; İnce, M.; Oral, O.; Süer, G.; Almasarwah, N.; Singh, M.; Salah, B. Application of Machine Learning Methods for Pallet Loading Problem. Appl. Sci. 2021, 11, 8304.

AMA Style

Aylak BL, İnce M, Oral O, Süer G, Almasarwah N, Singh M, Salah B. Application of Machine Learning Methods for Pallet Loading Problem. Applied Sciences. 2021; 11(18):8304.

Chicago/Turabian Style

Aylak, Batin Latif, Murat İnce, Okan Oral, Gürsel Süer, Najat Almasarwah, Manjeet Singh, and Bashir Salah. 2021. "Application of Machine Learning Methods for Pallet Loading Problem" Applied Sciences 11, no. 18: 8304.

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