## 1 Introduction  
Distribution centers (DC) perform many essential functions, such as receiving, putting away, cross-docking, order-picking, sorting and shipping hundreds of products [1]. Notwithstanding that all DC functions are essential, order picking, particularly manual-based order picking, is the most resource-intensive and represents one of the highest DC costs [2]. In manual order picking, order pickers receive work assignments, which specify the stock keeping units (SKUs) and other relevant attributes describing the order, such as the number of units per SKU, the physical storage address of each of the SKU (slot) and the weight of the SKUs. Order pickers travel around the DC to collect SKUs from their slots. Minimizing the order picker's travel distance is a crucial decision problem that has received considerable attention from researchers and practitioners [2; 3; 4], which includes optimizing picking routes, zoning, storage location assignment and order batching. The physical location of the SKU is a factor affecting the order picking routes. The SKU assignment to picking slots--a decision problem well known as SLAP--attempts to determine the best location of the SKU to optimize the order picking travel distance. The detailed literature reviews in [2; 3; 4] discuss several types of storage assignment policies, including random storage, closest open location storage, dedicated storage, full turnover storage, class-based storage and family grouping. From these policies, only family grouping considers the relationship between the SKUs. Family groupings are defined in [2] as a set of items, referred to in this paper as \"itemsets\", frequently found in the same order. Items belonging to the same itemset have storage locations close to each other. Itemsets are created using frequency-based methods [5; 6], quantity-based methods [7; 8; 9] and datamining-based methods [10; 11; 12]. Data mining methods have shown promising results since they discover the correlations between the SKUs contained in large volumes of data, particularly because high computing power capabilities are more accessible to run the necessary data mining algorithms. Previous research has largely used data mining for computing similarities between SKUs, for batching orders and for calculating the fitness of SKUs to slots. The focus of this paper is on the sequencing of SKUs based on data mining-generated groupings. This paper aims to develop a heuristic method to optimize the order picking travel distance based on two considerations: frequent itemset grouping and nonuniform SKU weights. The proposed heuristic uses association rule mining (ARM), a well-known data mining algorithm. The association-based postprocessing method assigns positively associated items, found by applying ARM, to slots in proximity. Furthermore, our heuristic also takes into consideration the weight distribution of the items to be picked up. (A) Frequent itemset grouping: The literature shows heuristic methods for SLAP, which typically assign SKUs with higher turnover rates to slots near the input/output (I/O) points [4]. Other approaches are based on family groupings. In this paper, we propose using a frequent itemset grouping. A frequent itemset is defined as a nonempty set of SKUs that meet a minimum threshold. The threshold sets a level of accepted correlation between SKUs that will belong to a family, and this is determined by how often the SKUs are ordered together. To the best our knowledge, no other papers found in the literature have taken this approach. (B) Nonuniform SKU weights: DCs handle items with significant differences in weights, which impacts the order picking travel distance [13]. As order pickers collect the SKUs from the prescribed slots, they build a work order forming a stack of SKUs with varying shapes and weights. The variation in SKU weights creates a particular disruption in the order picking travel sequence because there is a preference in building stacks with the heaviest SKUs at the base and mounting less heavier items on top of the base.