# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Miscellaneous
There is a recognized need to consider real-world scenarios beyond just adding new items, such as order removals and changes. Lastly,to ensure the method's real-world applicability, future validation should extend beyond simulations to real warehouse environments. In their innovative study, [PERSON] et al. [92] propose a new system for inventory picking and shelf sorting, known as the ShelfMigrant AGV system. This system employs a real-time holonic scheduling method, aimed at minimizing the waiting time of pickers within a warehouse setting. They evaluated the proposed system using a simulator and found that it improved picking productivity by reducing the waiting time of pickers compared to the conventional system. The proposed system is suitable for large warehouses with orders with higher variation and lower volume. In future work, the authors plan to reduce the computational cost of the system and test it in actual warehouses. Overall, the authors' work provides a new approach to improving picking productivity in large warehouses with mixed and smaller order volumes using the ShelfMigrant AGV picking system. The approach was validated using a warehouse simulator. The primary innovation lies in its use of AGVs to transport both inventory and sorting shelves directly to pickers. Unlike conventional systems where sorting shelves are stationary or moved only after sorting tasks are complete, the ShelfMigrant AGV system can transport a sorting shelf even in the midst of its sorting, optimizing productivity. The research, while promising, is currently validated only through simulation. This means the results, while indicative, are yet to be proven in real-world conditions. There is also a recognition of computational costs associated with the proposed system, suggesting potential scalability or efficiency challenges. Future endeavours by the authors should focus on reducing the computational cost of the ShelfMigrant AGV system, making it more efficient for real-world application. Furthermore, the system needs to be tested in actual warehouse environments to validate its practical effectiveness beyond simulation. In the publication by [PERSON] et al. [93], an intelligent agent-based framework is introduced to enhance warehouse management systems (WMS) in dynamic demand environments. The authors provide a novel framework that offers a triple contribution, encompassing innovative design principles and approaches for efficient WMS implementation. First, it provides the benefit of detecting warehouse exceptions in real time before they escalate to disruptions. Unexpected events can have a significant impact on productivity and efficiency, despite adherence to warehouse processes that dictate a smooth flow of product. Second, research offers new algorithms and optimization processes to improve the efficiency of warehouse processes, but these are not easily incorporated into existing WMS systems. Constructing service agents in this model can facilitate the addition of new features in an environment where they can be applied as required. This application is based on real-time environmental constraints, and the features can be evaluated both in real-time and historically through data analysis. Third, the decision support aids warehouse decision makers by addressing cognitive, memory, and time constraints. It does this by providing exception information packages and potential solution scenarios, which facilitates faster resolution of warehouse exceptions that require human involvement. The framework has been tested on a warehouse simulation. The authors have innovatively developed an agent-based Warehouse Management System (WMS) framework, meticulously tailored to address the complexities of dynamic e-commerce landscapes. This framework encapsulates features such as real-time warehouse exception detection, a modularized mechanism for facile integration of contemporary optimization algorithms via service agents, and an intricate decision support infrastructure designed for expeditious human intervention.