# Application of Machine Learning Methods for Pallet Loading Problem
###### Abstract
Other studies have focused on the use of Artificial Intelligence (AI) and Machine Learning (ML) methods to schedule and control freight loading operations, supply chain management, and logistics in the manufacturing industry [11; 12; 13; 14]. This research study originated as a part of the research initiative by the DHL supply chain, which focuses on determining the optimal classification methods to load boxes on the pallets, also taking into account the humidity and storage duration. It was reported that the stability, the strength of the pallet, and the mechanical strength of the box are adversely affected by changes in humidity [15]. DHL operates in more than 220 countries and territories globally, and it is one of the world's major shipping companies. It delivers goods to more than 120,000 destinations from its 5000 offices, using more than 76,000 delivery vehicles [16]. Hence, it is absolutely imperative for DHL to drive state-of-the-art research to find efficient solutions to the PLP to ensure its smooth operation. The ultimate objective of any algorithm addressing the PLP is to arrange a given number of homogeneous or heterogeneous-sized boxes in an optimized manner in terms of space or costs in a certain container or pallet. To solve a PLP, several performance measures, such as pallet utilization, the stability and strength of the pallets, the constraints related to humidity or storage duration, etc., are considered, irrespective of the aforementioned categories. Generally, the stability of the pallets increases when their utilization improves. Therefore, many studies have focused on maximizing the utilization of pallets to improve overall stability. In general, it was reported that the multi-pallet loading problem is an NP-hard problem, and researchers have used non-linear integer programming or stochastic search methods to solve it [5]. The non-linear method guarantees an optimal solution, but it is a computationally expensive method, especially if the parameters in the formulation increase [17]. Contrarily, stochastic search methods provide a sub-optimal solution at a reduced computational cost [5]. In this paper, a novel ML approach was employed for solving PLP to provide a suggestion framework to evaluate the optimal position of a box in a pallet. In the proposed methodology, two -phase algorithm is developed based on a dataset from the DHL supply chain to load identical boxes onto the pallet. The first phase maximizes the number of boxes per horizontal layer, where five heuristics, one block, two-block, three-block, hollow-block, and G5 heuristics are utilized. The second phase determines the number of the horizontal layers loaded per pallet based on the maximum height, maximum weight and dynamic strength. More specifically, various ML methods, such as Support Vector Classifier (SVC), Decision Tree Classifier (DTC), K Nearest Neighbour Classifier (KNN), Random Forest Classifier (RFC), and Artificial Neural Network Classifier (ANN), were used on the dataset, and the categories of the existing pallet types were evaluated. This ML-based approach to solve PLP provides better results compared with running various genetic algorithms or heuristics each time. The results show that the optimal pallet selection process may take several hours when executing these traditional algorithms, compared with \(\sim\)1 min after employing the ML methods. The results of this study report that a very high (>85%) Pallet Utilization Volume (PUV) can be obtained using ML models on the given dataset. Moreover, an accuracy of >89% was found to predict the accurate loading arrangement of boxes on a suitable pallet. The remainder of the paper is organized as follows. A literature review is presented in Section 2, providing the background research studies and methods. In Section 3, the research methodology and classification methods are elaborated. Subsequently, the data analysis is carried out in Section 4. The results are presented and explained in Section 5. Finally, Section 6 provides the conclusion and future work.