# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 1 Introduction
The choice of technologies associated with Industry 4.0, combined with other types of established and affordable technologies, form the proposed path to cover the storage needs of a small polyethylene wax production company that uses low molecular weight polyethylene that has been obtained as a by-product of ethylene manufacturing. The academic literature associated with block stacking in warehouses is mainly focused on the design of optimal layouts and generally takes into consideration shared storage policies, avoiding the relocation of pallets [17] either due to a drive-in-pallet racking type storage system [18] or due to other planning-dependent factors [19]. This type of analysis is associated with storage spaces in which pallets located in the same lane belong to the same typology. Under these premises, it is possible to determine the number of aisles that maximize space utilization while minimizing handling costs [20]. These particular cases can be seen in [PERSON] et al. [21] or [PERSON] and [PERSON] [22]. Some later approaches have considered that the inflow of products does not follow a perfect staggering, finding a reduction in waiting times and space costs [23]. The use of block stacking, and therefore the required pallet relocations needed for picking, has not been deeply studied in warehouses handling pallets that have unique characteristics. For certain types of \"stock keeping units\" (SKUs), as in this case, it is one of the few existing storage options. It requires low investment and, above all, allows a high degree of layout flexibility and correct and reliable localization inside warehouses, for example, using RFID-equipped forklifts capable of recognizing loaded pallets and self-locating [24] or advanced methods of monitoring forklifts [25]. In this sense, internal transport becomes a possibility to improve warehouse management. The real possibilities of implementing machine learning in industries that use recycled raw materials, in which each of the SKUs involved is unique, forces the study of the behavior of this type of warehouse under another perspective and individualized management. In this context, mature technologies such as RFID, capable of facilitating logistics time savings, can still extend their capabilities and be useful in big data environments [26], among other reasons, because the capabilities of the tags associated with them are also expanding [27] and the signal acquisition systems skills [28]. This option is compatible with the technological advances in indoor assets location [29] and allows the initiation of practical steps towards a digital warehouse in an immediate operation way. Simulations aimed at aspects such as product flow [30], the efficiency of robotized operations [31], the efficiency of flexible manufacturing systems [32], or sustainability [33] have already shown their results and can be transferred to the industry later. Moreover, a system or platform that allows extension and personalization of the behavior of objects would allow the creation of a data library with possibilities of previous analysis in less time [34]. These aspects are important because the tools developed in different modeling elements are separated from each other and without connection between them [35], and this could hinder decision-making at all levels of the company [36]. For these reasons, software that facilitates its use on several platforms facilitates its subsequent integration with the physical system. A mathematical support for simulations can be the network flow that allows defining nodes and arcs and finding optimal flows of products and vehicles, among others [37]. This versatile method has been used in warehouse design considering the routing of order pickers and certain peculiarities in the storage facility or product; for example, it is used to find the shortest order picking tour in a warehouse with a middle aisle [38].