930. Optimising Rolling Stock Maintenance: A Simulation-Driven Scheduling Approach
Invited abstract in session MB-28: Simulation and System Dynamics, stream Decision Support Systems.
Monday, 10:30-12:00Room: Maurice Keyworth 1.03
Authors (first author is the speaker)
| 1. | Cansu Kandemir
|
| Advanced Manufacturing Research Centre, The University of Sheffield |
Abstract
Rail depots manage crucial operations such as inspections, cleaning, refuelling, and maintenance. However, limited space and the unidirectional movement of rolling stock make efficient train sequencing essential to avoid delays and disruptions. Rolling stock maintenance further complicates operations, impacting railway safety and reliability. Increasing maintenance-related train delays, as shown in government reports and railway statistics, underscore the urgent need for optimised maintenance scheduling and depot train movement strategies.
This study introduces an integrated framework that combines Discrete Event Simulation with a maintenance scheduling optimisation model, developed through an ontology-based virtual depot scheduling tool. By anticipating operational bottlenecks, predicting maintenance and movement conflicts, and dynamically adjusting schedules, this approach improves depot efficiency, reduces disruptions, and enhances system resilience. The framework enables proactive decision-making, ensuring smooth train movements while minimising delays and optimising resource allocation, thereby improving system reliability, passenger satisfaction, and cost efficiency.
Its predictive capabilities make the railway system more adaptive, resilient, and capable of handling anticipated operational challenges, ensuring long-term efficiency and reliability.
Keywords
- Capacity Planning
- Scheduling
- Simulation
Status: accepted
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