# Wave Planning for Cart Picking in a Randomized Storage Warehouse
## 3 Problem Description and Assumptions  
### Problem Description  
Despite the fact that warehouses have been around for hundreds of years, they continue to evolve because of dramatic changes in customer demands, along with the constant advancements in technology. This is leading to a revolution in warehouse capabilities. According to [16], the global trends of warehousing businesses include (1) higher workforce expectations, (2) continued focus on regulatory compliance, (3) next-generation postponement strategies, (4) increased use of automation and robotics, (5) geographic expansion, (6) increased presence in urban area, (7) compressed order processing times, (8) grater internal and external collaboration, and (9) smart optimization. Basically, to conquer these global trends of warehousing businesses, an enterprise can tackle them by developing new supply chain collaboration strategies, by developing new logistics automation devices, or by developing intelligent mathematic models or algorithms to optimize the warehousing/logistics resources. In this study, we concentrate on tackling the challenge by smart optimization. This trend of growing online shopping sales has significantly influenced the goods supply chains throughout its international, regional, and last mile segments. Kang [23] pointed trade and logistics businesses have restructured operational and system-wide aspects of their supply chain to accommodate the ever-growing demand for just-in-time (JIT) production, online shopping, and instant shipment/delivery. To apply JIT concept to order picking process, a number of researches have been tackled jointly in previous studies to provide a comprehensive solution to order picking [6; 10; 11; 12; 13; 24; 25], fulfilling a specified objective function to reduce operational costs [11; 12; 26; 27; 28; 29], and enhance customer service [10; 21; 30; 31]. We summarized previous studies, that provide integration solutions for order picking processes, as shown in Table 2.  
Recall the case study, stocking packed delivery boxes generated every 2 h is not an efficient way of managing a warehouse. It is not only increasing total travelling paths of all picking operators, but also might waste warehouse buffering space. The idea to improve warehousing efficiency of this case study is to apply JIT concept to order picking process in the 2-h express warehousing company. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Integration** & **Benefi/Goal** \\ \hline [PERSON] et al. [12] & Order batching, Picker routing & Improve order picking \\ [PERSON] et al. [33] & Order picking, Sequencing & \\ \hline [PERSON] et al. [34] & Order picking, Vehicle routing & Provide high-quality solutions. \\ \hline [PERSON] et al. [4] & Order batching, Delivery & Deliver numerous orders within the shortest time \\ \hline [PERSON] and [PERSON]*[6] & Order batching, Order picking & Optimize customer response time. \\ \hline [PERSON] et al. [10] & Order batching, Sequencing, Routing problem & Minimum total tardiness of customer orders. \\ [PERSON] [31] & Order batching, Sequencing & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The integrated studies in recent years. Try to think about this. Does it make sense to stack packed delivery boxes generated every 2 h (i.e., 2-h based wave planning)? If we can plan order picking waves to as close to as their loading due time as possible, the time with the packed delivery boxes stacking at the buffering space will be reduced, the buffering space for stacking packed delivery boxes will be reduced, and the processing time window from customer orders to picking orders can be expanded, too. With the expanding time windows, more similar order list can then be scheduled into this time frame. The efficiency of the picking operator can be improved.