Route Guidance System Using Machine Learning for Courier Pairing
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Solution Overview
Problem
Existing methods for path selection using vehicle route guidance suffer from inaccuracy in predictions, which affects the efficiency of alimentary combination assembly and delivery processes.
Innovation Solution
A method and system that utilize machine-learning processes to compute projected nutritionally guided order volumes and determine assembly times, generating predicted routes and optimizing courier pairings based on objective functions to improve route efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path selection methods are used, then the system is simpler to implement, but the prediction accuracy and route optimization are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/path-based route selection with machine learning models that process nutrition data, order volumes, and courier information to generate optimized routes. The ML systems substitute for manual or rule-based path selection, achieving higher accuracy through data-driven predictions rather than simple geometric or time-based calculations.
Solution Approach 2:
The system changes the parameters used for route selection from basic distance and time to include nutritionally guided order volumes, assembly times, and courier performance metrics. By transforming the input parameters and using them in ML models, the system achieves more precise predictions while managing complexity through structured data processing.
2Productivity
If assembly times are not optimized, then the process is simpler, but the courier downtime increases and delivery efficiency decreases
Solution Approach 1:
The system performs preliminary computation of assembly times using machine learning models before couriers begin their routes. By predicting assembly times in advance based on nutritionally guided order volumes and historical data, the system enables better route planning and reduces courier waiting time, thereby improving overall delivery efficiency.
Solution Approach 2:
The system uses feedback from actual assembly times and courier performance data to continuously refine ML model predictions. This feedback loop allows the system to learn from real-world operations and improve its ability to predict assembly times, reducing courier downtime over time while maintaining operational simplicity.
3Reliability
If real-time courier pairing with routes is implemented, then the route optimization improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The system segments the route assignment process into distinct ML models: one for predicting nutritionally guided order volumes, another for assembly times, and a third for courier-route pairing optimization. This segmentation allows each model to specialize in specific aspects, improving overall accuracy while managing computational complexity through modular processing.
Solution Approach 2:
The system transforms raw operational data into optimized parameters for route assignment by using ML models to predict key metrics. This parameter transformation enables more reliable courier-route pairing decisions while reducing the computational burden of processing raw data in real-time, as the ML models pre-process and structure the information.
Data Source
AI summary
A system for path selection using vehicle route guidance includes computing device configured to receive a plurality of requests for a plurality of alimentary combinations and a plurality of destinations, wherein each request specifies an alimentary combination of the plurality of alimentary combinations to be assembled by at least an alimentary provider and a destination of the plurality of destinations, compute a projected nutritionally guided order volume using a first machine-learning process, determine a plurality of assembly times, the plurality of assembly times including an assembly time for each alimentary combination, as a function of the nutritionally guided order volume, generate a plurality of predicted routes as a function of the determined assembly times, and pair a predicted route of the plurality of predicted routes with a courier, wherein pairing further pairing, with the courier, the predicted route that optimizes an objective function.