### The State-of-the-Art Design of Palletized Load Units
The authors addressed a novel 3D-BPP variant in which the shape-changing factor of non-rectangular and deformable items was incorporated into the model. In the research of [PERSON] et al. [14], another practical variant of 3D-BPP is addressed. The authors analyzed the mixed-case palletization problem, where item support with the presence of different sizes of items is considered. [PERSON] et al. proposed a novel problem formulation as well as a column-generation solution approach. [PERSON] and [PERSON] [15] concentrated on the design of an approximate algorithm of BPP applicable to solve large-scale instances within a reasonable time. They proposed a modified version of the squirrel search algorithm (SSA) for solving the 1D bin packing problem. In the experiments, hard class instances of up to 200 items were tested, and the obtained result was compared against other approximate algorithms such as particle swarm optimization (PSO), African buffalo optimization (ABO), and crow search algorithm (CSA). A typical complex decision problem is usually a combination of packing and vehicle loading problems [3; 4; 15]. In the work of [PERSON] et al. [3], the optimal solution to the problem is presented. However, the mathematical model is not presented in detail. This concept is applied to solve the combined problem of pallet and vehicle loading, and the size of unit packages and the size of pallets and vehicles are also discussed. A two-phase approach with a packing problem is also considered by [PERSON] and [PERSON] [16]. In this approach, however, the main objective is to guarantee sufficient utilization of the truck loading space. [PERSON] et al. [4] considered the problem of building and placing pallets on the truck at the same time, i.e., pallet loading as the first phase and truck loading as the second phase. The authors implemented several extended constraints, including the total weight of the load, the maximum weight supported by each vehicle's axle, and the distribution of the load inside the vehicle. During the truck loading phase, it was allowed to stack one pallet on top of another. The model was constructed and solved with the GRASP algorithm, and the experiments were performed in the domain of the number of instances, vehicles, and computation time. [PERSON] and [PERSON] [2] have also proposed a two-phase procedure. In the first phase, 2D layers were defined, while in the second phase, the layers were combined on the minimum number of pallets. The authors constructed a specialized metaheuristic with a MILP model of a 3D problem, which was subsequently solved by the Gurobi solver. The experiments were performed in the domain of the number of instances and computation time. Contrary to the previous research on BPP and vehicle loading problems, [PERSON] et al. [17] proposed the research on BPP with compatible categories, i.e., products that cannot be transported together. In this approach, the bin is the fleet of vehicles, and the problem is to allocate products with respect to the product categories. [PERSON] et al. concentrated on large instances of 200-1000 items, and they solved them with a variable neighborhood search (VNS) procedure. The experiments were also compared with alternative procedures. There are several conclusions resulting from the state-of-the-art of the bin packing problem that should be highlighted, i.e.,Numerous papers on BPP focus on the analysis of a wide spectrum of constraints that are either theoretical or practical. There is hardly any research on small-size pallets, i.e., substantially below the maximum height or weight of the PLU. It is a typical situation in contemporary logistics. * The BPP is an NP-hard decision problem, which is why a substantial number of studies have been carried out on the effectiveness of the optimization procedures. * The case when PLUs can be stacked one on top of another is referred to as a combined problem of pallet and vehicle loading. It is not, however, a widely discussed topic in the literature.