Vehicle Dispatch Using Machine Learning Models
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Solution Overview
Problem
Current dispatch systems for mobile aid and service vehicles primarily rely on geographical proximity, neglecting other critical factors that impact response time, such as vehicle capabilities, availability, and travel time.
Innovation Solution
A machine learning-based system that utilizes a dispatch database to maintain vehicle and constraints data, allowing a processor to identify the most appropriate vehicle for a dispatch request by considering multiple factors including location, capabilities, and availability, using a machine-learning model to determine the optimal vehicle for dispatch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dispatch systems rely solely on geographical proximity to select vehicles, then the selection process is simple and fast, but response time efficiency deteriorates because other critical factors like vehicle capabilities and actual travel time are ignored
Solution Approach 1:
The system transforms the dispatch decision from a simple distance-based selection to a multi-parameter optimization problem. The machine learning model evaluates multiple parameters simultaneously including vehicle capabilities, availability status, current location, and predicted travel time to determine the optimal vehicle assignment, thereby improving response time efficiency without manual complexity
Solution Approach 2:
The patent replaces traditional rule-based dispatch mechanics with a machine learning-based decision system. Instead of using fixed rules that prioritize only geographical proximity, the system employs trained models that automatically process multiple factors and generate optimized dispatch decisions, substituting complex manual or rule-based evaluation with intelligent automation
2Measurement precision
If multiple factors are considered in vehicle selection, then dispatch accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical dispatch data before actual dispatch operations. This pre-processing allows the models to quickly evaluate multiple factors during real-time dispatch decisions without performing complex computations from scratch, thereby maintaining high selection accuracy while minimizing processing time loss
Solution Approach 2:
The system uses historical dispatch data and vehicle performance patterns as training copies to build predictive models. These models capture complex relationships between multiple factors and optimal vehicle selections, allowing the system to make accurate decisions by applying learned patterns rather than重新 analyzing all factors for each dispatch request
Data Source
AI summary
A dispatch database maintains, for a plurality of vehicles available for dispatch, vehicle data and constraints data. A processor is programmed to execute a dispatch server to perform operations including to receive a dispatch request requesting a vehicle to arrive at a request location, utilize a machine-learning model to identify one or more of the plurality of vehicles to respond to the dispatch request, the machine-learning model utilizing the vehicle data and the constraints data as inputs to determine the one or more of the plurality of vehicles, and inform the one or more of the plurality of vehicles of the dispatch request.