# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Algorithm-Based Methods
The authors have introduced the Interventionist Routing Algorithm (IRA), marking a significant innovation in warehousing strategies. A standout attribute of the IRA is its unique ability to start order-picking directly from within an aisle, necessitating the development of novel initiation procedures and arc configurations to accommodate forklift operations. While simulations underscore its potential, real-world validation remains pending. Future research could delve into its adaptability in diverse warehousing scenarios or compare it with emerging order-picking methodologies. The publication by [86] proposed an intelligent agent-based model for solving the order picking problem in an industrial warehouse with multiple storage locations. The authors combined elements of hierarchical and heterarchical frameworks in the modelling framework, resulting in a hybrid model. To overcome the rigidity and inflexibility of the hierarchical model, the authors used a real-time task assignment negotiation mechanism. Lower-level agents negotiate within the boundaries set by higher-level agents, resulting in both horizontal and vertical negotiation. Optimization algorithms, learning, databases, and knowledge bases were used by the agents to make better decisions. The authors used the proposed framework to solve the problem of allocating orders to picking zones through intelligent agents. Results using real system data showed that the proposed framework provided better throughput than the hierarchical model. This demonstrated flexibility, robustness, and fault tolerance to unforeseen events such as machine failure. Overall, this publication presents a novel approach to solving complex order picking problems in industrial warehouses using intelligent agent-based models. The authors have introduced a groundbreaking model tailored to address the order picking problem in industrial warehouses with multiple storage locations. This novel approach employs a real-time task assignment negotiation mechanism to surmount the inherent structural rigidity of traditional hierarchical models. While the proposed framework has been validated with real system data and exhibits superior throughput compared to the standard hierarchical model, the study's results are still limited to simulations and specific datasets. It remains to be seen how this model responds across varied warehouse configurations or in environments with different unpredictabilities beyond machine breakdowns. Future avenues could delve into a broader validation across varied warehouse layouts, incorporate cutting-edge AI and machine learning for enriched agent decision-making, and probe the model's synergy with burgeoning technologies such as IoT and robotics. In their valuable research, [PERSON] et al. [87] put forth a real-time routing methodology for warehouses with multiple order pickers. The approach is based on an online Ant Colony Optimization (ACO) algorithm, which demonstrates its potential in managing congestion within the warehouse environment. The method aims to optimize picking routes for multiple pickers, taking into account congestion and unstable picking times. For each picker, a default route is generated by ACO and then coordinated to avoid congestion during the picking service. The proposed method improves the overall service time and throughput of the order picking process. A simulation was conducted to evaluate the effectiveness of A-MOP-NPT, which showed promising results in dealing with congestion and increasing system throughput. However, it should be noted that the current study assumes a pick-by-order policy and no capacity limit for pickers, which may not reflect real-world scenarios. The researchers innovatively employed the Ant Colony Optimization (ACO) algorithm to manage warehouse congestion. They generated a default route for each picker, minimizing congestion. However, the approach assumes a pick-by-order policy and overlooks picker capacity limits, limiting real-world applicability.