A common solution in warehouses is to provide direct access to products due to various service policies. The construction of such warehouses is quite expensive due to the extensive service roads required, but at the same time, in accordance with the Pareto principle, most of the variety of materials does not justify the provision of direct access. We believe that similar dynamics could be achieved on a smaller area with the right operation of an indirect access storage system, as if direct access were provided to all stored units. Meanwhile, the high variety of products and the low accessibility make it a challenge in selecting and implementing the right operation method. The efficiency is influenced by the layout of the warehouse, the assignment of stored units to storage locations, and the operating processes. We conducted simulations to examine these; the results will be presented here. The design of the warehouse and the stochastic material flow was not changed during the examinations; the initial arrangements of the stored materials and the procedures for their movement were built according to several aspects. This revealed how these factors affect efficiency, the computational capacity needed, and the mutual influence they exert.

The aim of this paper is to investigate the structure of the Hungarian automotive supply network and provide a possible solution that mathematically describes the connections between the interested parties. In the study an approximate model is introduced to determine the links between hubs (car manufacturers), nodes (Tier1 suppliers) and edges, combining probability random graph and scale free network theory. During the simulation some main drivers were applied for selection purposes, such as location, turnover, product profile. As a result of the study a potential tool has been designed to support decision-making.

Traditional storage systems often rely on direct access storage locations, but these solutions can be inefficient and environmentally exhausting due to higher energy consumption and limited scalability. In previous studies, we demonstrated that higher efficiency could be achieved with indirectly-accessible storage systems compared to fully directly accessible ones. To compare the different layouts, we developed a simulation model through which warehouse operations were executed. The resulting time values became comparable, allowing us to rank the solutions. However, the conventional method of calculating simple averages, often applied in the evaluation of logistic performance, has limited capacity to capture the complexity of data distributions and the small differences between individual observations. The aim of this study is to interpret warehouse operations from a new perspective using a fuzzy logic-based model, explicitly addressing the research gap that traditional statistical averages cannot adequately capture subtle performance differences. The novelty of our contribution lies in applying fuzzy signatures to warehouse simulation, which enables more precise differentiation between competing system layouts. During the simulation analysis, traditional average values are compared using a classification method based on fuzzy sets, demonstrating that this new approach is more sensitive to performance fluctuations and better reflects real operational conditions. This approach supports the selection of the most suitable storage system for warehouse operations, the initial placement of products, or the operational process that best aligns with emerging demands, enabling greater energy savings and enhancing the overall sustainability of the system.