In a flexible multi-tray manufacturing system for intelligent warehousing, optimizing pallet transport paths is crucial for improving production efficiency and reducing operating costs. This process requires comprehensive consideration of equipment layout, order characteristics, dynamic interference, and system coordination, using intelligent algorithms and real-time feedback mechanisms to dynamically adjust paths. Its core objective is to shorten pallet movement time between processes, reduce empty load rates, avoid path conflicts, and ensure smooth operation during multi-pallet parallel production.
The system first needs to establish a three-dimensional spatial model, mapping elements such as equipment, storage areas, and buffer zones in the manufacturing workshop as digital nodes, and marking the connection relationships and access capabilities of each node. For example, the locations of key equipment such as welding stations, painting lines, and assembly stations need to be accurately recorded, along with the transport distance, turning angle, and potential obstacles between adjacent nodes. This model provides the basic framework for path planning, enabling the system to quickly generate initial path plans based on real-time order demands. Initial path generation typically employs shortest path algorithms from graph theory, such as Dijkstra's algorithm or A* algorithm, to initially determine the pallet's movement trajectory by calculating the minimum cost path between nodes. Differences in order characteristics place higher demands on path optimization. Different pallets may require different processing steps or have specific load-bearing capacity requirements for conveyor equipment. For example, large pallets need to avoid narrow aisles, while precision pallets require preferred conveyor routes with less vibration.
The system needs to extract key information such as pallet size, weight, and process route through the order parsing module and input this information as constraints into the path planning algorithm. At this point, the initial path may be broken down into multiple sub-paths, each corresponding to specific conveyor equipment or process steps. The system must ensure seamless connections between these sub-paths to prevent pallets from stalling or backflowing when switching equipment.
Real-time handling of dynamic disturbances is a challenge in path optimization. Factors such as equipment failure, temporary material stacking, or personnel movement on the manufacturing floor may obstruct the original path. The system needs to monitor the conveyor environment in real time through a sensor network, such as using LiDAR to scan for obstacles in the aisles or RFID tags to track pallet positions. Once a path conflict is detected, the system immediately initiates a local replanning mechanism to search for alternative paths around the affected area. This process must balance efficiency and stability, for example, prioritizing proven reliable paths or assessing the potential risks of new paths through simulation. If local replanning fails to resolve conflicts, the system sends a request to the upper-level scheduling module to coordinate the delivery order of other pallets, achieving global optimization.
Path coordination during multi-pallet parallel production is another key challenge. When multiple pallets enter the conveyor system simultaneously, a lack of coordination in path planning can lead to congestion or equipment overload. The system needs to employ either a centralized or distributed scheduling strategy. The former involves a central controller uniformly allocating path resources, while the latter allows each pallet to make its own decisions but adheres to global rules. For example, the system can assign priority tags to each pallet, with higher-priority pallets occupying key channels first, or use a time window algorithm to distribute pallet delivery times across different time periods to avoid concentrated traffic. Furthermore, the system must reserve a certain proportion of redundant paths to handle sudden demand or equipment failures.
The effectiveness of path optimization requires continuous feedback and iterative improvement. The system records the actual delivery time, energy consumption, and fault information for each delivery and compares this with the theoretical model to identify deviations in path planning. For example, if the actual delivery time for a certain path consistently exceeds expectations, it may be due to equipment aging causing speed reduction, or the path design failing to consider actual terrain undulations. The system adjusts model parameters or replans routes based on this feedback data, forming a closed-loop optimization mechanism. This adaptive capability allows the system to gradually adapt to changes in the production environment, such as seasonal order fluctuations or the introduction of new equipment.
Ultimately, the intelligent warehousing flexible multi-tray manufacturing system achieves efficient, stable, and dynamically optimized pallet transport routes through the above strategies. This process not only improves the transport efficiency of a single pallet but also maximizes the utilization of manufacturing resources through the collaborative scheduling of multiple pallets, providing solid support for flexible production.
