One-to-Many Path Search Using Heuristic Boundary Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing job scheduling systems face significant computational challenges and resource requirements when determining travel costs for multiple job locations, leading to prolonged processing times and inefficiencies.
Innovation Solution
Implementing a one-to-many shortest path search method using a graph search algorithm, such as A* search, which calculates travel costs by determining heuristic costs based on geographic distances and adjusting a boundary to focus the search, thereby reducing computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Dijkstra or A* algorithms are used to compute travel costs for each job location individually, then accurate travel cost calculation is achieved, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent combines multiple individual shortest path searches into a single one-to-many search operation. By treating multiple destination nodes simultaneously in one graph traversal, the system calculates travel costs from one origin to many destinations in a single computational pass, rather than performing separate Dijkstra or A* searches for each destination, thereby reducing total computational time while maintaining accuracy
Solution Approach 2:
The patent segments the search space by identifying a boundary node that divides the graph into regions. This segmentation allows the algorithm to focus computational effort on relevant paths by using the boundary node as a reference point for heuristic calculations, improving efficiency when computing travel costs to multiple destinations
2Measurement precision
If traditional Dijkstra or A* algorithms are used to compute travel costs for each job location individually, then accurate travel cost calculation is achieved, but computing resource requirements increase
Solution Approach 1:
The patent merges multiple individual shortest path computations into a single unified one-to-many search operation. By processing multiple destination nodes simultaneously within one graph traversal framework, the system reduces the total computational work required compared to performing separate Dijkstra or A* searches for each destination, thereby lowering computing resource consumption while preserving calculation accuracy
Solution Approach 2:
The patent introduces boundary node segmentation to divide the graph into manageable regions. This segmentation enables more efficient heuristic calculations and reduces the search space that must be explored, decreasing the computational resources needed while maintaining accurate travel cost determination
3Measurement precision
If scheduler initialization and appointment booking processes use comprehensive travel cost calculations, then scheduling accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies one-to-many shortest path search to simultaneously calculate travel costs to multiple job locations in a single operation. This merging of computational tasks accelerates the scheduling initialization process and appointment booking operations while maintaining the accuracy needed for optimal schedule generation
Solution Approach 2:
The patent uses boundary node segmentation to efficiently organize and process travel cost calculations during scheduling operations. By segmenting the search space around boundary nodes, the system rapidly computes accurate travel costs for multiple destinations, improving both scheduling accuracy and processing speed
Data Source
AI summary
Methods, systems, and computer readable media for scheduling jobs by using a one-to-many shortest path search based on travel costs between job locations. In some examples, a system includes one or more computers configured to schedule jobs based on travel from a geographic origin to a respective geographic job location for each job. The system also includes a one-to-many path searcher implemented on the one or more computers. The one-to-many path searcher searches for paths along edges of a graph from an origin node to destination nodes by traversing intermediate nodes between the origin node and the destination nodes using travel costs, including determining heuristic travel costs based on a geographic distance between an intermediate geographic location corresponding to the intermediate node and a boundary that initially surrounds the geographic job locations.