Dual-Network Vehicle Fleet Routing System
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Solution Overview
Problem
Current route selection algorithms for vehicle fleets are inefficient in minimizing distance and time, particularly at intersections, due to the complexity of evaluating all possible routes and constraints such as traffic, fuel efficiency, and road classifications.
Innovation Solution
The implementation of a groove routing system that uses a simplified routing network to quickly identify a groove route, which is then refined using a more exhaustive routing network, considering factors like distance, traffic, and street classifications to prioritize routes and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive routing network evaluating all possible routes is used, then route optimization accuracy is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The routing network is segmented into a coarse routing network and a fine routing network. The coarse network provides a simplified overview for quick route identification, while the fine network provides detailed evaluation for accurate optimization. This segmentation allows the system to process routes efficiently without evaluating every possible path in detail.
Solution Approach 2:
The system performs preliminary routing using the coarse routing network to identify candidate routes before conducting detailed evaluation. This preliminary action filters out suboptimal routes early in the process, reducing the computational burden of comprehensive evaluation while maintaining optimization accuracy for the final route selection.
2Measurement precision
If all routing constraints and factors are evaluated, then route optimization quality is improved, but device complexity and computational requirements increase
Solution Approach 1:
The routing evaluation process is segmented into multiple passes: initial route identification using the coarse network, followed by progressive refinement using the fine network. This segmentation allows comprehensive constraint evaluation to be performed systematically without overwhelming computational complexity at any single stage.
Solution Approach 2:
The system performs partial evaluation by focusing computational resources on evaluating only the most promising candidate routes identified by the coarse network, rather than exhaustively evaluating all possible routes. This partial action maintains optimization quality while reducing overall computational complexity.
3Productivity
If a simplified routing network is used for quick route identification, then processing speed is improved, but route accuracy and optimization quality deteriorate
Solution Approach 1:
The dual-network architecture segments the routing problem into two distinct evaluation stages. The coarse routing network provides fast, simplified route identification to establish candidate paths quickly, while the fine routing network subsequently refines these candidates with detailed evaluation to ensure accuracy. This segmentation allows the system to achieve both speed and precision.
Solution Approach 2:
The coarse routing network acts as an intermediary that bridges the gap between speed and accuracy. It processes routing requests quickly to generate candidate routes, which then serve as input for the more accurate fine routing network. This intermediary layer enables the system to maintain high processing speed while preserving route accuracy through subsequent refinement.
Data Source
AI summary
A vehicle management system is described herein that can perform a variety of features related to managing fleets of vehicles. For example, the vehicle management system includes a routing module that can perform a variety of functions. Some of these functions can include determining a first route between locations using a simplified routing network and then determining a second route between the locations using a more exhaustive routing network. Further, the routing module can store information about the natural flow through intersections, improving control of routes through intersections. Some or all of these features may be used together, or any of these features may be omitted in any given embodiment.