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3266. A Heuristic Approach for Real-Time Adjustment of Disruption Programs - Applied to the Context of Commuter Railway Systems
Invited abstract in session MA-54: Disruption management in passenger railways, stream Public Transport Optimization.
Monday, 8:30-10:00Room: S01 (building: 101)
Authors (first author is the speaker)
| 1. | Cedric Steinbach
|
| Department of Civil and Environmental Engineering, Technical University of Darmstadt, Institute of Railway Engineering |
Abstract
Disruption on commuter railway systems with significant capacity restrictions (e.g. line closures) lead to substantial effects on the planned operations.
One strategy to manage occurred disruptions is based on the use of pre-structured operating concepts, prepared to deal with generic disruption scenarios, so-called disruption programs (DRP). Since DRPs are planned offline for specific scenarios (e.g. failure of a certain group of infrastructure elements, like signal boxes), they allow dispatchers to react more effectively to the occurred event.
If a disruption affects a combination of infrastructure elements that are not explicitly considered within an existing DRP, the full potential of the DRP cannot be utilized. To support dispatchers in responding to disruptions, an ad-hoc adjustment of the DRPs to the availability of the infrastructure has been developed.
The approach determines the need, develops and introduces necessary adjustments to existing DRPs. At its core, the adjustment is a problem of combinatorial optimization, which is solved using a heuristic method. By combining components of different metaheuristics, a problem-specific solution approach based on a local search was developed.
The approach assesses the adjustments based on their impact on the resistance of the passengers' connections and ensures the operational feasibility of the adjusted DRP. To illustrate the approach, it is applied to an example of a disruption in a commuter railway system
Keywords
- Railway Applications
- Scheduling
- Decision Support Systems
Status: accepted
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