# AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse
## 2 Literature Review  
The activity of order picking, that is the manual or automated withdrawal of stock keeping units from storage locations to satisfy customer demand, is a frequently object of study in the literature [3; 4]. It is in fact among the most labor-intensive and costly activities in any material handling system [1]. In traditional picker-to-parts manual warehouses, the items, are collected by human operators (order pickers) who operate by moving from one shelf to another [2]. A common configuration for the management of order picking activities in the warehouse consists in its subdivision in zones (we speak in this case of Zone Picking [5]). This strategy is particularly efficient to reduce the risk of congestion and improve picking performance, both in automated and manual warehouses [6]. On the other hand, once the collection operation has been completed, there is the need to group together items of the same order, still spread over several areas; this last aspect can be a significant disadvantage. There are two different approaches in Zone Picking systems: parallel (synchronized) and sequential (progressive) [6]. In the first case, pickers employed in different areas of the warehouse simultaneously collect items of the same order and the items are put together once each picker has completed her/his picking process. In the second case, however, after a picker has collected all the items of a certain order in her/his area, those are transferred to the next area, typically by means of conveyors; in the situation described, therefore, items of the same order are already grouped together. Several authors have studied the optimization of a warehouse organized in zones [7]. The choice of the optimal number of zones to divide a manual warehouse was analyzed in [8; 9], with the aim of minimizing the collection time. The authors of [10], in addition, investigated the problem of assigning robots to different zones, demonstrating that the processing time of an order can be reduced by a third, sharing robots among them. Finally, the authors of [11] analyzed the relationship between the storage activities, Order Batching, Zone Picking and Routing, demonstrating that, for greater efficiency, all the steps indicated should be managed in an integrated manner. The studies mentioned above refer to systems in which the zoning is static and therefore not modifiable during the picking operations. The research of most of authors focused on the minimization of the crossed total distance or of the time spent from the operator in its activity of retrieving orders from the warehouse and other environments (see, for example, [12; 13; 14]). From other authors, instead, a solution approach has been proposed, aimed at the minimization of the total delay (tardiness). In [15] the total delay is defined as the positive difference between the completion time of an order and its due date. Orders, in fact, must be completed within set time limits, in order not to cause delays in production or delivery to the final customer. The authors listed below have studied the problem of the minimization of the total delay in various contexts of warehouse. The authors of [16] considered the JOBASP (Joint Order Batching, Assignment and Sequencing Problem) for the case of a single picker in an automated warehouse. The same problem was treated in [17], but in the case of a non-automated warehouse. Subsequently, in [18] the analysis is extended to the case of multiple pickers, and heuristic approaches (see [19]) are employed for large instances. [PERSON] et al. [20], then, proposed an integrated solution between the problems of Order Batching, Batch Sequencing and Picker Routing, aimed at minimizing the total delay. Finally, [PERSON] et al. [21], presented a solution for a zoned warehouse where human operators are assisted by AMR automated carts. In recent years, automated components have become preponderant in warehouses, with the aim of speeding up operations and employing the working time of the human operators more efficiently [22].