Street Level Routing Using Ant Colony Optimization
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Solution Overview
Problem
The challenge of finding an optimal travel route for field service representatives (FSRs) is computationally impractical due to the complexity of street-level routing, which involves numerous permutations of potential paths and is typically NP-complete, making it inefficient to solve through traditional means.
Innovation Solution
The implementation of Hybrid Street Level Routing using the Ant Colony Optimization (ACO) algorithm, which pre-calculates weighted costs of transitioning between cells based on major road networks and distance, allowing for efficient and optimized route calculation while considering business-specific parameters like travel time, distance, and technician restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing algorithms are used to determine optimal routes, then route optimization is attempted, but computational complexity becomes impractical due to the NP-complete nature of the traveling salesman problem
Solution Approach 1:
The patent divides the continuous geographic space into discrete grid cells, transforming the complex continuous routing problem into a manageable discrete optimization problem. This segmentation allows the system to precalculate transition costs between cells and use heuristic search algorithms rather than evaluating all possible permutations of routes.
Solution Approach 2:
The system performs preliminary actions by precalculating transition costs between all pairs of grid cells before actual routing is needed. This precomputation stores cost matrices that capture terrain difficulty, distance, and other factors, allowing rapid route determination without recalculating these fundamental costs during real-time routing operations.
2Ease of operation
If straight paths are used for travel, then movement is simplified, but efficiency is reduced due to roads, traffic, and various impediments
Solution Approach 1:
The patent applies local quality by assigning different transition costs to different grid cells based on their local characteristics such as terrain type, presence of roads, traffic conditions, and other impediments. Each cell's transition cost reflects its specific local conditions, allowing the routing algorithm to naturally prefer efficient paths while still using a standardized grid-based framework.
3Measurement precision
If street level routing with numerous path permutations is attempted, then route accuracy is improved, but computational practicality is lost
Solution Approach 1:
The system changes parameters by transforming the routing problem from considering continuous coordinates and infinite path possibilities to working with discrete grid cells and finite transition options. This parameter transformation maintains sufficient route accuracy for field service representation while making the problem computationally tractable through dynamic programming and heuristic search methods.
Data Source
AI summary
A system and method for street level routing includes dividing a mapping of a geospatial region into cells, each cell having one or more pathways located within the cell. A traversability weighting is determined for each of the cells, based at least on a traversability measure associated with the one or more pathways located within the respective cell. Total weights associated with one or more routes for traversing every cell to every other cell are pre-calculated, and the pre-calculated total weights are used to determine one or more routes between a first point and a second point in the geospatial region, with one or more preferred routes determined in accordance with a ranking of the one or more routes between the first cell and the second cell, the ranking based on one or more efficiency criteria.