Abstract
Disruption management is of high importance to the railway operating companies facing the increasing passenger demand. Therefore, train dispatchers need to respond to disruptions occurring in daily railway operations by rescheduling and rerouting trains. In this study, we address integrated train rescheduling and rerouting problem, which can be implemented in operational level planning. The integrated railway scheduling and routing problem is formulated as a Modified ParallelMachine Job Shop Scheduling (MPM-JSS) model to minimize total delay in the railway network. A novel hybrid metaheuristic which combines a Variable Neighborhood Search (VNS) algorithm with Tabu search (TS), called VNS-TS, is developed to solve the mentioned problem. We conduct computational experiments to evaluate the performance of the suggested optimization model and the VNS-TS algorithm. The computational experiments investigate a real-world case study of London Bridge area in the UK railway network, which considers different disruption scenarios including blocked tracks on a singletrack section, blocked tracks on multiple-track sections, and longer running or dwell times. The results show the effectiveness of VNS-TS algorithm in terms of the solution quality compared to the optimization
model implemented by CPLEX.
model implemented by CPLEX.
Original language | English |
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Pages | 68 |
Number of pages | 1 |
Publication status | Published - 12 Jul 2021 |
Event | 31st European Conference on Operational Research (Euro 2021): The 2nd International Workshop on 'Artifical Intelligence for RAILwayS' in 2021 (AI4RAILS2021) - Duration: 11 Jul 2021 → 14 Jul 2021 |
Conference
Conference | 31st European Conference on Operational Research (Euro 2021): The 2nd International Workshop on 'Artifical Intelligence for RAILwayS' in 2021 (AI4RAILS2021) |
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Period | 11/07/21 → 14/07/21 |
Keywords
- metaheuristics
- optimization modeling
- railway applications