# Application of Machine Learning Methods for Pallet Loading Problem
###### Abstract  
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. logistics; machine learning; pallet loading problem (PLP); classifiers +
Footnote †: journal: _Sci. The first is the uniform PLP, which is generally applied for identical or homogeneous types of boxes, whereas the second is termed the mixed PLP, which categorizes non-identical types of boxes [3]. Furthermore, this classification of boxes can be further categorized on the basis of the box sizes in the system, termed as weakly heterogeneous and strongly heterogeneous [4; 5]. Additionally, as a rule, there can be no overlap and overhang for the boxes to be loaded onto the pallets. The Pallet Loading Problem is undeniably a complex and multifaceted task [6]. [PERSON] et al. [6] studied the complexity of an arrangement of cargo in a pallet and proved through principal component analysis and multiple linear regression that the number of boxes is a strong predictor of complexity in this challenging problem. Moreover, the Distributor's Pallet Loading Problem (DPLP) is an NP-hard problem and has multiple layers of complexity stemming from various constraints imposed on it, such as the stability of a layer to sustain upper layers, the compression limit of each layer to sustain the weight of the layers placed on top of it, and the weight limit imposed by the company carrying out the packaging [7]. Nonetheless, loading boxes onto a container in such a manner that the 3D volume is utilized to the maximum is also a computationally demanding problem since it requires addressing various constraints, such as the orientations in which a box can be loaded as well as stability constraints [8]. Finally, cargo security during the transportation process needs to be thoroughly accounted for, and it can be enhanced through the selection of a proper stretch film and an appropriate cargo wrapping model [9]. For many industries, logistics activities such as transportation and storage play a key role in delivery, cost reduction, and the safety of goods. For this purpose, pallets are utilized as the most significant strategic equipment in the logistics of shipment and delivery. Similarly, another important factor is to utilize an optimal number of pallets for any freight, which may save an enormous amount of costs [1]. Consequently, many recent studies have focused on solving the PLP for different 3-dimensional (3D) pallet sizes to reduce the total number of pallets as well as the optimal sorting of boxes in an efficient manner [10].