Graph Neural Network Logistics Route Planning
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Solution Overview
Problem
Current logistics route planning methods are inefficient due to high computational complexity in large graphs, reliance on domain knowledge, and inability to adapt to real-time incidents, limiting their ability to find optimal paths and predict delivery times accurately.
Innovation Solution
A graph neural network-based system that learns node and edge embeddings to determine optimal paths by considering real-time incidents, allowing for flexible adaptation to changes in the logistics network without retraining, and providing scalable and user-friendly decision support for planners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shortest path algorithms (Dijkstra, Bellman-Ford, A*) are used to find optimal paths, then path optimization is achieved, but computational complexity becomes too high for large graphs
Solution Approach 1:
The patent replaces traditional mechanical graph traversal algorithms (Dijkstra, Bellman-Ford, A*) with a neural network-based system. The neural network learns to predict optimal paths by analyzing graph structures and incident impacts, substituting the computational mechanics of classical algorithms with neural computation that scales more efficiently to large graphs while maintaining path optimization accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of path finding from exact mathematical optimization to probabilistic prediction. Instead of guaranteeing optimal paths through rigorous algorithms, the neural network provides path probabilities based on learned patterns from graph data and incident information, enabling efficient processing of large graphs with dynamic conditions.
2Ease of operation
If logistics simulations with digital twins are used to predict delivery times and investigate scenarios, then planning support is improved, but the models require domain knowledge and predefined models that may not reflect real-world situations
Solution Approach 1:
The neural network system performs self-learning from graph data and incident information without requiring predefined domain models. The system automatically adapts to real-world situations by learning patterns from the actual logistics network structure and incident impacts, eliminating the need for manual model configuration while maintaining high planning support capability.
Solution Approach 2:
The system incorporates feedback loops where the neural network continuously learns from graph data and incident information to improve its path prediction accuracy. This feedback mechanism enables the model to adapt to real-world conditions dynamically, improving both planning support and adaptability without requiring manual model updates.
3Measurement precision
If manual path finding using domain knowledge and human intuition is used, then approximate optimal paths can be identified, but time consumption is high and there is no guarantee to find the optimal path
Solution Approach 1:
The patent replaces manual human reasoning with neural network computation. The neural network processes graph data and incident information automatically, providing path probability predictions in seconds rather than requiring human analysts to manually evaluate multiple routes. This substitution maintains high path optimality while dramatically reducing time consumption.
4Productivity
If graph neural network methods are used to directly take the graph as input for path optimization, then path finding is improved, but the models can only be applied to fixed graphs since any change requires retraining
Solution Approach 1:
The patent implements dynamic adaptability in the neural network system. The model can process varying graph structures and incident configurations without retraining by using a flexible architecture that learns from diverse graph data during initial training. This enables the system to adapt to dynamic changes in logistics networks while maintaining high path finding efficiency.
Data Source
AI summary
A graph database stores a knowledge graph having nodes and directed edges representing a current status of a logistics network, wherein the nodes include entity nodes each representing an entity in the logistics network, and incident nodes each representing an incident, and wherein the directed edges include delivery edges and impact edges. For determining an optimal path between a sender and a receiver a graph neural network model provides node and edge embeddings. Neural networks receive the embeddings and calculate for the respective entity nodes and delivery edges a probability that they lie on an optimal path. A greedy pathfinding algorithm computes the optimal path using the calculated probabilities. This approach takes the impact of real-time incidents into account and serves as a decision support for production planners and logisticians to identify optimal and alternative paths and make better informed planning choices.