AI Vehicle Routing for Revenue-Maximizing Multi-Task Scheduling
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Solution Overview
Problem
Current systems for vehicle delivery management lack the ability to automatically optimize routes and maximize revenue, as they rely on users to choose tasks individually and often restrict concurrent jobs, leading to inefficient use of vehicles and missed revenue opportunities.
Innovation Solution
A vehicle routing system that utilizes artificial intelligence and deep learning to analyze vehicle definitions and task parameters, generating optimal routes that maximize revenue by scheduling a list of tasks for vehicles, while also processing sensor data to update routes and provide analytics to users through a management hub app.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually choose individual delivery jobs, then they have flexibility in job selection, but revenue maximization and time efficiency deteriorate due to lack of optimization
Solution Approach 1:
The system enables vehicles to automatically select and optimize their own delivery routes using AI algorithms. The vehicle routing system autonomously processes available tasks, evaluates them against vehicle definitions and constraints, and generates optimal routes without requiring manual user intervention for each job selection, thereby maintaining operational flexibility while dramatically improving revenue efficiency
Solution Approach 2:
The patent replaces manual human decision-making (mechanical selection process) with an AI-based automated routing system. The system uses machine learning models and optimization algorithms to automatically evaluate and select delivery tasks, substituting the manual job-selection mechanism with an intelligent computational system that maximizes revenue while respecting user-defined constraints
2Device complexity
If systems restrict concurrent jobs, then job management complexity is reduced, but time efficiency and revenue opportunities deteriorate due to sequential task execution
Solution Approach 1:
The system dynamically adjusts route generation to accommodate concurrent jobs by creating multiple optimized routes simultaneously for different vehicles or time periods. The AI routing engine processes task availability in real-time and generates dynamic route schedules that allow vehicles to perform multiple tasks concurrently or in optimized sequences, transforming the static sequential job management into a dynamic parallel processing system
Solution Approach 2:
The system performs preliminary analysis and route generation for multiple potential concurrent jobs before execution. By pre-processing task definitions, vehicle availability, and route options, the system prepares optimized concurrent job schedules in advance, allowing vehicles to efficiently execute multiple tasks without real-time management complexity during actual operation
3Productivity
If AI and deep learning functionality are used to generate optimal routes, then revenue maximization improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex routing optimization problem into manageable components: vehicle definition processing, task definition retrieval, route generation, and performance evaluation. The AI system processes these segments in a structured pipeline, allowing computational complexity to be distributed and managed efficiently while still achieving revenue maximization through comprehensive optimization
Data Source
AI summary
A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.