Dynamic Vehicle Routing for Revenue-Maximizing 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, often restricting concurrent tasks and relying on users to choose jobs individually, 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 application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select individual jobs or tasks, then users have control over job selection, but revenue maximization and time efficiency are compromised
Solution Approach 1:
The system enables self-service by implementing an automated routing system that autonomously selects and assigns tasks to vehicles based on predefined criteria and real-time data, eliminating the need for manual user intervention while optimizing revenue generation and operational efficiency
Solution Approach 2:
The patent replaces the mechanical manual selection process with an automated computing system that uses algorithms to analyze task parameters, vehicle availability, and revenue potential, substituting human decision-making with computational optimization to maximize productivity
2Device complexity
If systems restrict concurrent jobs, then task management becomes simpler, but time efficiency and revenue opportunities are lost
Solution Approach 1:
The system implements dynamic task management that allows concurrent jobs by continuously monitoring vehicle status, task requirements, and environmental factors, dynamically adjusting routing and task assignment to handle multiple simultaneous operations without overwhelming complexity
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously receives real-time data from vehicles and the environment, processes this information through routing algorithms, and adjusts task assignments and routing accordingly, enabling efficient concurrent job management through closed-loop control
3Device complexity
If static routing is used, then route planning is simpler, but adaptability to environmental changes and task modifications is reduced
Solution Approach 1:
The system transitions from static to dynamic routing by continuously updating task assignments and vehicle routes based on real-time environmental data, task status changes, and vehicle availability, allowing the routing plan to adapt dynamically without requiring complex manual replanning
Solution Approach 2:
The patent implements preliminary action by pre-establishing routing algorithms and decision-making frameworks that are ready to execute when environmental changes or task modifications occur, enabling rapid adaptation through pre-programmed responses rather than reactive planning
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.