# A Survey of the Literature on Order-Picking Systems by Combining Planning Problems
## 5 Discussion
### Investigating the Problems of Order-Picking Planning and Their Combination
#### 5.1.2 Study of the Components of the Order-Picking Process
A Pareto chart was also created to understand the effect of the assignment policy on the targeted activities to ultimately both provide a quantitative approach to integrate the rate of energy consumed at the estimated time for the entire OPS and introduce rest and unemployment time. On the other side, by considering the manpower capacity and examining the operator preparation time, [PERSON] et al. (2019) stated that in addition to reducing costs, OPS also aims to improve system performance. Furthermore, various forms of assignment of the official and effective model in warehouse areas with comparative numerical capability have been developed. For example, in a study entitled \"Maverick picking\", [PERSON] et al. (2019) argued that having sufficient curiosity for new issues such as modifying the workflows in customized picking is a significant challenge for most warehouse managers. The purpose of the above research was to describe the Maverick picking process, its executive roots, and shape to demonstrate its potential effect on the order-picking process. This would be important since order-picking is a centralized work process and picking is a centralized and costly activity for most warehouses. It should be mentioned that 55% of the total warehouse costs related to the order-picking process. They also declared that this share would be allocated to optimal interior design, warehouse assignment methods, routing methods, order batching, and zoning (2019). [PERSON] et al. (2019) tried to develop an integrated model to improve ergonomic and economic performance. This study developed an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we can study the behavior of individual order pickers and their interactions with the environment. In the present study, we considered travel time as a distance-dependent item and stated that travel time increased by distance; thus, travel distance could be a significant goal in designing and optimizing order-picking warehouse. [PERSON] et al. (2019) showed the distribution percentage of order-picking time types as shown in Figure 8.  
Figure 8: Distribution Percentage of Order picker Time Types (2019). Due to recent trends in orders, [PERSON] et al. [4] examined the need to deliver orders in the shortest time and proposed different mathematical models to improve performance. They also examined various factors to reduce pick time. Figure 8 shows the most effective measures taken to reduce pick time by focusing on strategies related to reducing travel time, search, and pick time. Moreover, a study entitled \"a discrete channel design model for an order-picking warehouse\" attempted to develop a model to reduce the travel distance for picking operations, and thereby lowering the cost of operations. To this end, the concept of warehouse design with large discrete channels was introduced, where there is a linear transverse corridor called a tunnel and an efficient algorithm is used to calculate the tunnel [41]. In this article, research was conducted on how to increase the effectiveness of order-picking using integrated warehousing, branding, regional picking, and routing policy decision-making: Delivering parts on time reduces search time and component selection. In addition, in [PERSON] [64], a study on storage location assignment and optimization of order-picking in the automotive industry, the assignment and order of the collection system were developed by using mathematical modeling and random evolutionary optimization.