## 2 Literature Review
The author formulates a mathematical program to create the clusters, and a primal-dual solution approach was followed to solve the clustering model. The author also uses simulations to evaluate the layout solutions. [PERSON] et al. [6] define the \"between-items associations\" as the probability that a pair of items are ordered together. The authors note that [PERSON] [7] ignores the association between the items, so they formulate a two-stage Clustering Assignment Problem Model (CAPM) to determine the optimal SKU storage locations. A mathematical model assigns products to groups based on a high association between the products in the first stage. In the second stage, a mathematical model assigns the products to storage locations based on order frequency. Their experiments show that this two-phase clustering approach reduces order picking times when compared to the random storage. [PERSON] [8] proposes a Quadratic Generalized Assignment Model. This model receives as inputs the similarity between SKU, the throughput-to-storage ratios, the distance between pairs of storage slots and the distance from the input/output point to the storage slots. Since this model is NP-hard, the author proposes a novel heuristic to achieve a quick practical layout using a three-step solution approach: (1) rank items based on ordering frequency, (2) cluster items based on their similarities and (3) interchange storage locations to minimize order pick time. [PERSON] [9] extends the work by [PERSON] [8] to account for nonuniform density SKU. The author highlights that it is frequently overlooked to analyze retail stores' ordering practices related to how items are ordered together. Our paper is motivated by this premise, and it tries to close the gap by using a data mining approach to discover groups of items that are ordered frequently. [PERSON] et al. [10] introduce data mining for order picking and propose using a popular market basket analysis tool known as ARM algorithms to form the family of correlated products. As the author notes, most methods to determine relationships between the SKUs are frequency-based or quantity-based. [PERSON] et al. [10] propose a similarity coefficient that considers the distance and the turnover rate of the SKUs, the frequency that a pair of SKUs occur together in an order and the complementary/substitutive relationship between SKUs. [PERSON] et al. [11] propose two heuristics that use a weighted support count to measure the intensity and the nature of the relationship between pairs of products. The first heuristic, called the modified class-based heuristic, divides the distribution center into classes. Each class contains products with a similar turnover rate and maximizes products' associations with storage locations by considering one class time. The second heuristic, called the Association Seed-Based Heuristic, maximizes associations of products stored in the same aisle by considering one aisle at a time. Their experiments show that these two \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Metric** & & **Remarks** \\ \hline [PERSON] et al. [12], [PERSON] et al. (This paper) & Support. Lift. Itemsize. & & Data Mining-based method. Itemset size = 2 or more. Consider the relationship between any number of items (itemset). Confidence is a fixed parameter. Conduct a simulation study to evaluate order picking time as function of storage location solutions and order picking routes. Present a real case study using a dataset from industry. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._heuristics outperform other methods based on the class-based heuristic. [PERSON] et al. [12] developed ARM-based sequencing-based storage location assignment heuristics for item slot selection and sequencing order picking routes. The slot selection heuristic assigns the most desirable slots (i.e., those closest to the input/output points) to items with high demand, and then locates highly correlated items closer to each other. The route sequencing policies explored by the authors are flexible.