## 5 Conclusions  
This paper proposes the use of an ARM-based heuristic for solving SLAP. The proposed heuristic consists of the following main steps: (1) dividing the SKUs into weight \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Order Picking**} & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} \\ \cline{2-5}  & **Class-Based** & **ARM-Based** & **Class-Based** & **ARM-Based** \\
**(in Seconds)** & **Layout** & **Layout** & **Layout** & **Layout** \\ \hline Average & 3345.4 & 3006.9 & 3489.0 & 3010.8 \\ \hline Standard & 221.1 & 180.1 & 257.4 & 235.0 \\ Deviation & 0.066 & 0.060 & 0.074 & 0.078 \\ \hline Maximum & 3722.1 & 3213.8 & 3767.1 & 3213.8 \\ \hline Mininimum & 2755.1 & 2603.6 & 2623.3 & 2259.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of class-based versus ARM-based layouts. Figure 6: Comparison of class-based versus ARM-based layouts for Scenario 2.  
classes, i.e., heavy, medium and light; (2) choosing levels of support and confidence to generate rules with the Apriori algorithm; (3) extracting frequent itemsets; and (4) generating a layout with SKU locations placing frequently ordered itemsets near to each other. The higher the frequency with which items are ordered, the closer they are located to the input/output point. The resulting layouts were tested to estimate the total order picking times. The datamining approach was applied to a real case. The layouts generated by ARM resulted in lower average order picking times than those layouts generated using class-based layout. The ARM layout seeks faster order picking times by considering itemsets that are ordered frequently, not just individual items. For the operation in this study, the amounts of data are vast and are available from a database across the entire enterprise. The body of orders data is constantly increasing in number of transactions, number of items and in the diversity of types and sources. Therefore, the method proposed in this study requires hardware and software capable of storing and processing large sparse matrices of data. The authors recommend an implementation using a software environment such as R [18], Python [22] or Julia [23]. All three environments offer at least one implementation of the Apriori algorithm. This study has shown how this data mining algorithm can be applied efficiently and effectively in a real setting to discover trends in data useful to increase delivery speeds, and ultimately cut logistical costs. For instance, this paper applied the ARM technique to discover the family of correlated SKUs that should be assigned to slots in proximity. During the conduct of this study, the authors devised three paths for further research. First, instances that the authors have designated as singletons. Singletons are items that are not grouped with any others as a result of the mining processes. These instances represent an inefficiency in the implementation of ARM, which has not been mentioned in the prior literature. During the experimentation, it was observed that the appearance of a singleton could force the picker to deviate far from the I/O point, thus markedly increasing the order picking completion time. These particular items can cause disruptions in the design of the order picking routes. The opportunity is in the development of algorithms that minimize the effects of the singletons, seeking to improve the efficiency of the order picking process and possibly improving the performance of ARM. A second extension of this study concerns the implementation of ARM to cases in which the demand pattern of high-selling items is not stable. In this method, high-selling items that sell together greatly influence the resulting items' locations in the warehouse. An unstable demand of such itemsets would imply a periodically changing layout. Lastly, in designating the item locations, this study only considers the loading process.