# Application of Machine Learning Methods for Pallet Loading Problem
## 2 Literature Review
Another study on the PLP solution was carried out by [PERSON] et al. [27] using an exact algorithm based on the depth-first strategy. Their novel methodology was based on the idea of the reduction of the size of the search tree. Thus, the final solution effectively utilizes the available system memory. In this way, more effective pruning can be performed. In the literature, there are several hybrid methods as well, which combine the classical and heuristics methods. For instance, a hybrid approach coalescing the heuristic and genetic algorithms was developed by [PERSON] et al. [5] to solve the PLP for profit optimization. Subsequently, this hybrid approach was compared with two stochastic search methods, Simulated Annealing (SA) and Tabu search (TS), as well as a nonlinear integer programming-based branch-and-bound method. The results of the comparative analysis revealed that the proposed hybrid method was more cost-effective than stochastic search methods. In another research study, a hybrid method was utilized in [PERSON] and [PERSON]'s [28] study, which employed new bounds, heuristics, and exact algorithms. The proposed method was able to determine an optimal solution for approximately 99.7% of the problems. The remaining 0.3% comprised of just one box varying from the optimal solution. [PERSON] et al. 's [29] study aimed to revolutionize the warehouse design through elevating the routing optimization problem of two forklifts operating in a four-door warehouse, considered as a quadratic assignment problem. [PERSON] and [PERSON] [30] investigated how and at what cost freight containers could be used as an inventory-holding mechanism for humanitarian logistics. Most of the above-mentioned solutions to the PLP have the limitations of either being computationally expensive for large problem sizes or providing sub-optimal solutions. In our proposed solution for the PLP, an ML-based two-phase algorithm was developed in order to solve the 3D PLP comprising of identical boxes. In addition, the proposed solution considers the storage time and humidity without any pallet overlap and overhang [10]. Furthermore, in our proposed study, five heuristics, i.e., the one-block heuristic, three-block heuristic, five-block heuristic, hollow block heuristic, and G5-heuristic, were used to evaluate the total number of identical boxes per horizontal layer and the corresponding box loading layout or pattern on the pallet [28; 31; 32]. As an addition to the second phase of our previous study [6], the number of horizontal layers per pallet is computed on the basis of the dynamic compressive strength of the pallet for each of the heuristics. Consequently, the total number of boxes per horizontal layer and the total number of horizontal layers are used to determine the number of boxes per pallet for each of the aforementioned five heuristics. In the final stage, the box-loading layout that furnishes the maximum number of boxes per pallet is preferred. To the best of our knowledge, no prior study evaluated the solution for the PLP using ML methods. The models were trained and tested on real-world data acquired from the DHL supply chain; therefore, the results of this study can be significantly utilized in any similar research study. We could not compare the results since no study using traditional optimization could be found in the literature for pallet loading. Therefore, we believe that this is an original contribution to the literature.