Fleet Allocation via Virtual Service Lines
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Solution Overview
Problem
Current fleet allocation methods in transportation networks are inefficient due to fixed vehicle allocations across service lines, leading to underutilization in sections with varying demand, and computational complexity increases exponentially with consideration of potential sub-lines, hindering demand-responsive route optimization.
Innovation Solution
A method employing multi-start sequential genetic searches with penalty terms to allocate vehicles to both original and virtual service lines, allowing flexible allocation and reducing operational costs by generating virtual lines that represent segments of physical service lines, thereby optimizing vehicle utilization across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed vehicle allocations are used across service lines, then operational simplicity is maintained, but vehicle utilization efficiency deteriorates due to underutilization in sections with varying demand
Solution Approach 1:
The patent segments the transportation network into multiple service lines with dynamic vehicle allocations. Instead of treating the fleet as a single fixed allocation, the system divides vehicles into different groups assigned to different service lines based on real-time demand, allowing each segment to operate independently with optimized resource levels.
Solution Approach 2:
The patent implements dynamic fleet allocation where vehicle numbers assigned to each service line can change over time based on demand fluctuations. The system continuously monitors demand patterns and adjusts vehicle allocations dynamically, transforming the static fixed allocation model into a flexible dynamic system that adapts to varying operational conditions.
2Manufacturing precision
If potential sub-lines are considered for optimization, then route optimization accuracy improves, but computational complexity increases exponentially
Solution Approach 1:
The patent extracts and focuses only on the most relevant service lines and routes for optimization rather than considering all potential sub-lines. By identifying and isolating key routes that contribute most to overall system efficiency, the system achieves meaningful optimization results without the exponential computational burden of evaluating every possible route combination.
Solution Approach 2:
The patent applies optimization with varying intensity to different parts of the network based on local demand characteristics. High-demand corridors receive more intensive optimization analysis while low-demand areas use simpler allocation rules, allowing the system to achieve high overall accuracy without uniformly applying complex computations across the entire network.
3Reliability
If centralized optimization approach is used to allocate vehicles, then demand satisfaction at line-level improves, but adaptability to local demand variations deteriorates
Solution Approach 1:
The patent enables different service lines to have customized vehicle allocation strategies tailored to their specific demand patterns. Each service line can implement local optimization rules that reflect its unique characteristics, such as peak hours, route complexity, and passenger flow patterns, allowing the system to simultaneously maintain high demand satisfaction and local adaptability.
Solution Approach 2:
The patent performs preliminary demand analysis and pre-positions vehicles at strategic locations before peak demand periods occur. By anticipating demand patterns and preparing allocations in advance, the system ensures both reliable demand satisfaction and rapid responsiveness when local demand variations occur, eliminating the trade-off between centralized planning and local adaptability.
Data Source
AI summary
A method for automatically allocating a plurality of available vehicles to a plurality of original service lines and virtual service lines of a transportation network includes receiving the virtual lines from a virtual line generator; approximating a constrained fleet allocation problem with an unconstrained fleet allocation problem that utilizes penalty terms to penalize violation of constraints; performing a multi-start sequential genetic search using a first population to identify a first solution; generating, using the first solution, a second population; performing a second multi-start genetic search using the second population to identify a second solution; and dispatching vehicles to different routes and lines according to the second solution.