Route Scheduling Using Packet Segmentation for Transit Fleets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for scheduling para-transit trip requests onto available vehicles fail to efficiently determine the optimal assignment of trips, leading to impractical computation times and inability to compare route solutions against quality metrics in a reasonable timeframe, especially with large volumes of requests.
Innovation Solution
A method that schedules packets of trip requests onto partial vehicle shifts, using a heuristic selection process atop a deterministic constructive process, allowing for the comparison of multiple solutions to select those that better meet quality metrics such as deadhead time minimization and work balance, by generating candidate schedules for subsets of half-shifts and committing the best schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deterministic constructive process is used to schedule trips onto vehicle routes, then the scheduling is systematic and complete, but the computation time becomes impractical and the system cannot compare route solutions against quality metrics in a reasonable timeframe
Solution Approach 1:
The patent segments the deterministic constructive scheduling process into multiple independent modules or components that can be executed in parallel. By dividing the overall scheduling problem into smaller sub-problems, the system can process multiple route solutions simultaneously, reducing total computation time while maintaining systematic completeness of the scheduling approach
Solution Approach 2:
The patent implements partial action by generating and evaluating a limited subset of candidate routes rather than exhaustively computing all possible routes. This allows the system to obtain sufficiently good solutions within practical timeframes by focusing computational resources on the most promising route options, thereby balancing scheduling completeness with computation time constraints
2Measurement precision
If all possible route solutions are generated and compared against quality metrics, then the optimal assignment is found, but the computational complexity becomes unmanageable with large volumes of trip requests
Solution Approach 1:
The patent applies local quality by evaluating and optimizing specific segments or portions of routes independently rather than treating the entire route network as a single complex problem. This allows the system to assess quality metrics for individual route segments or packets, maintaining optimization accuracy while reducing overall computational complexity through localized analysis
Solution Approach 2:
The patent implements preliminary action by pre-processing and filtering trip requests and route options before full optimization. By organizing data structures, pre-calculating feasible routes, and filtering out obviously suboptimal solutions in advance, the system reduces the complexity of the main optimization task while preserving the ability to find optimal assignments
Data Source
AI summary
A system and method for assigning trip requests to schedules for a fleet of one or more available vehicles in a manner that allows for the comparison of the various possible schedules against a number of global factors. Trip requests are combined into packets and one or more packets that can serve as a root packet or a first packet to be serviced by a partial shift are determined. Schedules are determined for each of the one or more possible root packets. The schedules are ranked according to one or more global factors to select a schedule for the partial shift. By generating combinations of packets that can be assigned to partial shifts, the problem of combinatorial explosion is reduced such that alternative schedules can be compared against one or more quality metrics for the schedule.