AI Route Stacking to Reduce Redundant Fleet Routes
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Solution Overview
Problem
Current fleet logistics systems are inefficient and wasteful, leading to increased costs, emissions, and time delays due to underutilized vehicles and redundant routes, particularly in school bus transportation.
Innovation Solution
A system utilizing an artificial intelligence engine to identify and reassign routes from one vehicle to another within a fleet, optimizing vehicle utilization by comparing routes to available groups based on location and time, and providing a graphical representation with color-coded indicators for efficient route reassignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If routes are assigned to individual vehicles without optimization, then each vehicle can complete its assigned routes, but redundant routes and underutilized vehicles increase costs and emissions
Solution Approach 1:
The patent combines multiple routes that are geographically proximal and temporally compatible into stacked route assignments, allowing a single vehicle to efficiently serve multiple routes that would traditionally require separate vehicles. This merging of routes eliminates redundant travel and underutilized vehicles, directly improving productivity while reducing fuel consumption and emissions.
2Reliability
If more vehicles are deployed to cover all routes, then service coverage is ensured, but fleet size and operational costs increase
Solution Approach 1:
The system dynamically assigns and reassigns routes to vehicles based on real-time availability, location, and route compatibility. This dynamic routing allows the fleet to adaptively cover all required service areas with fewer vehicles, ensuring complete service coverage while minimizing the quantity of vehicles needed through flexible, real-time route stacking.
3Productivity
If vehicles are reassigned to optimize routes, then vehicle efficiency improves, but system complexity increases
Solution Approach 1:
The route stacking system operates autonomously, automatically identifying compatible routes, evaluating vehicle availability, and executing reassignments without manual intervention. This self-service capability manages the inherent system complexity through automated algorithms that continuously optimize route assignments, improving vehicle efficiency while the system handles the computational complexity internally.
4Productivity
If routes are stacked for already utilized vehicles, then maintenance costs are reduced, but scheduling complexity increases
Solution Approach 1:
The system performs preliminary evaluation of route compatibility, vehicle availability, and temporal constraints before executing route stacking assignments. By pre-assessing these factors and identifying optimal stacking opportunities in advance, the system reduces maintenance costs through improved vehicle utilization while managing scheduling complexity through proactive planning and automated evaluation.
Data Source
AI summary
A system and method include a device associated with a user. The device may identify to a server computer a route to be reassigned from a first vehicle to a second vehicle among a fleet of vehicles. The server device may include an artificial intelligence engine which compares the route to be reassigned to a plurality of route groups associated with one of the administrator and the ride requestor. The server device may further identify one or more route groups which are able to service the route to be reassigned based on the artificial intelligence engine. The server device may transmit a graphical representation of one or more of the plurality of route groups which is proximally available in terms of location and time, as determined by the artificial intelligence engine to service the route to be reassigned with a color coded indicator. The server device reassigns the route.