## 1 Introduction  
Warehousing is one of the most important areas of a logistics system. It creates a connecting link between the production and customers. The primary goal of storage is to ensure the requirements directly for consumption, i.e., to satisfy the current needs of the customers. Storage can be understood as a deliberate interruption of the flow of material at a certain place and for a certain time in the so-called point of disconnection of the logistics chain. In this sense, materials and products are recorded as stocks in the warehouse. In this context, information on the condition, movement, transport, location of stocks, etc., is processed. An important part of storage is the protection against the external influences with which the stock comes into contact during the storage process [1, 2, 3]. Warehousing, in conjunction with other logistics activities, provides the necessary level of customer service to the company's customers at the lowest possible total cost. Storage serves as an important link between the manufacturer and the customer. We can define warehousing as a component of the corporate logistics system that ensures the storage of products--including raw materials, parts, goods in production, semi-finished products, and finished products--at their places of origin and between the place of origin and the place of consumption. It provides management with information on the condition, circumstances, and locations of stock products. In addressing the study presented, it was essential to utilize the knowledge focused on the field of the modeling and digitization of the processes and systems related to storage, logistics, and material flows. In this context, the works of the authors focused on the design creation [4], modeling and simulation of logistic processes [2, 3, 5, 6], and optimization of warehouse items using a genetic algorithm [7, 8], which proved to be useful. According to [9], the authors state that, in line with the global trends in material handling and storage, the design process should not only incorporate the use of equipment with the fastest drives but also take into account the energy and environmental aspects of the installed equipment. The authors dedicated their efforts to designing an energy efficiency model for mini-load automated storage and retrieval systems. This model positively impacts both the economic and environmental aspects by reducing the energy consumption and, consequently, CO\({}_{2}\) emissions. When dealing with warehouses, it is also necessary to consider other environmental aspects. This approach is discussed in Ref. [1], which focuses on the importance of a quality working environment, and Ref. [10], which deals with a comprehensive approach aimed at reducing the noise emissions from industrial operations and their subsequent impacts on the environment and human health. In another paper, the authors [11] performed an optimal warehouse location planning analysis to decide on the optimal storage location for each component. They implemented the optimization using hierarchical clustering and k-means clustering to formulate an optimal consolidated picking strategy. They developed a consolidated planning-based selection methodology to support the intelligent manufacturing of wireless modules. Executing a cluster analysis for picking lists was included in the third part of the proposed methodology: the analysis of the picking-list consolidation strategies, where the first part of the methodology is the analysis of the storage space demands and the second part is the analysis of the optimal storage location planning. In [12], the authors mainly dealt with the optimization of the order-picking activities in warehouses, with a focus on parts-to-picker using cluster-based analysis. Among other things, they included an extensive overview of the papers using a cluster-based storage allocation policy, which was also beneficial in the context of the processed article. Cluster analysis is also of great importance in wider research.