# Slotting Optimization Model for a Warehouse with Divisible First-Level Accommodation Locations
## 1 Introduction  
Nowadays, companies pay more attention to the management of their warehouses, seeking to optimize the location of their products in the facilities based on the remaining time before being dispatched. Their supply chains tend to be dynamic, responding to the high response times demanded by customers. This requires agile warehouse management given a higher product turnover. However, there are no two businesses that work in the same way due to the multiple factors that can affect the dynamics of a supply chain, where the operations of a warehouse play a fundamental role in determining operating costs and customer time response throughout the distribution chain [1]. In this sense, the order picking process figure as one of the fundamental processes within warehouse management. This can be defined as the process by which items in storage are handled to fulfill a specific customer order. This operation has been evaluated as the most critical in the supply chain: ref. [2] establish that this process involves approximately 55% of the total operating costs related to the warehouse activities. This process normally considers tasks such as searching, handling, and product transferring, tasks that represent, respectively, 20%, 50%, and 30% of the total time of order preparation. Travelling is considered an unproductive activity, which does not add value to the operation, and considering that this item represents the largest component of order times, it reveals an opportunity to improve the performance of storage centers. In recent years, companies have sought to reduce travel times through the use of technologies, such as advanced storage management software, flow racks, voice picking, radio frequency identification, among others, spending around 10% to 30% more per year for this purpose. This is consistent with the information found in the literature, from which it is estimated that less than 15% of the SKUs (Stock-Keeping Units) are correctly located [3]. Better solutions can result froma deeper analysis of the correct positioning of products within a warehouse based on the demand for each product. In order to address the problems described in the order picking process, the concept of slotting has recently been studied and analyzed in industry and specialized literature. In this sense, the slotting process seeks to address order picking by searching for an intelligent arrangement of products within the warehouse. Specifically, it is the correct assignment of SKUs to available storage locations, establishing the storage mode, the volume of space to be assigned, and the exact location for product storage [4]. Slotting aims to improve efficiency in order preparation and reduce operating costs [5]. For the Slotting process, it is necessary to answer two basic questions: (1) How to classify SKUs? and (2) How to assign classified SKUs to locations? [6]. According to [7], it is possible to use a variety of strategies to optimize the operational process when carrying out the location assignment. However, it is possible to distinguish three main strategies, which are classified as dedicated, class-based, and random storage. Dedicated storage consists of assigning a fixed slot for a certain item within the warehouse [4]; this strategy is aimed at quick access in manual systems, thus reducing search time and improving the handling and transfer of products by operators. In class-based storage, items are classified, grouped, and stored in certain slots according to their turnover rate, which enables higher handling or sorting throughput as a result of the connection between products, i.e., locations are assigned adjacently for those items that are usually ordered together. Random storage, in turn, results in higher utilization of warehouse capacity due to location assigning characteristics, resulting in a uniformly distributed allocation. However, the latter strategy can be cumbersome and sometimes expensive, which is why it is commonly used under the closest position rule [4].