## 3 Presentation of the Proposed Model
### The Model Description  
The developed model uses as the input data sales data (clients order-picking lists), stored products properties, and the warehouse layout. The input data are analyzed to find the correlation between products. Standard product classification methods (ABC analysis, XYZ analysis, Index COI), along with the authors' method for determining a relationship between products (frequency of occurrence in the clients' orders), are used.  
The proposed model is presented in Figure 1 as a block diagram.  
The results of the products classification are then compared with the products' parameters and the demand for the product by statistical analysis. Next, the best classification method and the most important classification criterion are used further for the automatic preparation of teaching data sets for the artificial neural network. After teaching the ANN, the simulations of product classification and allocation are executed. Furthermore, the performance analysis is accomplished, and the ANN structure is modified. The simulations, performance analysis and ANN structure modification are repeated in a sequence until the stop condition is reached (the improvement of the results is low, or the simulation time is too long). On reaching this condition, the model gives a value of the products classification and the product allocation layout.  
Figure 1: The block diagram of a model.  
The developed model has the features of artificial intelligence. It searches for correlations between the output and the results of the \"classical\" analysis, which are used for establishing a set of \"learning data and targets\" for the learning process of ANN. According to the results obtained from artificial neural networks (ANN), the model performs the simulation automatically, evaluates the efficiency of the obtained solution, and corrects the structure of the ANN. Work done by the analyst has been minimized, e.g., the introduction of parameters describing the structure of the warehouse and the selection of classification criteria (relevant for the particular warehouse).  
The main differences between the proposed model and models used in the literature so far are shown in Table 1.  
The comparison of the proposed method to the other method presented in the research paper is in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Difference Criteria** & **Proposed Model** & **Models Presented in the Literature** \\ \hline Number of analyzed criteria & Analyzing four criteria at the same time & Analyzing one or just a few classification criteria [7; 9] \\  & Model base on demand, sales & \\ Complexity & statistics, can be used for large assortments, a different type of warehouse and products & Basic—method uses a simple algorithm [1; 10; 11; 12] \\  & Artificial intelligence—artificial neural networks, statistics, simulation of picking time & Block algorithm 2, Genetic algorithm [10] \\ Analyzing picking distances & Yes & No 1  \\ Taking velocity into account & Yes—for a homogeneous type of racks & No 1  \\ The ability to adapt to changes in demand, assortment (with parameters) and storage conditions & Yes—automatic for each of them & For demand, and just for the basic data of assortment [13; 14; 15] \\ Analyzing the quality of the result & Yes—automatic analysis of results is based on simulation & No, just by additional works 1 \\  & List of products with parameters, products demand, warehouse structure, the velocity of forklifts, picking lists & List of products with basic parameters, products demand 2 \\ Output data & Product classification, location of products in the warehouse & Product classification 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between the proposed model and the standard models.