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Segment-condition-based railway track maintenance schedule optimization
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AI
DOI：10.1111/mice.12824.png)
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Abstract
En 中文
Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level with days as the time units. Second, they fail to consider the bidirectional feedback and dynamic impact between predicted condition and maintenance scheduling. Third, actual on-site operation strategies (e.g., opportunistic and centralized maintenance) are neglected. To address these issues, this paper proposes a novel operation-level maintenance scheduling framework based on segment condition considering both opportunistic and centralized maintenance strategies. According to the problem characteristics, ae hybrid multi-objective optimization algorithm based on a quantum-behaved particle swarm optimization was designed. Furthermore, by utilizing actual practical cases, the effectiveness of the model and algorithm was verified via comparison with MOPSO, NSGA-II, NSGA-III, and Gurobi in different scenarios.
Keywords:
PARTICLE SWARM OPTIMIZATION
2-PHASE GENETIC ALGORITHM
FRAME ROOF STRUCTURES
MULTIOBJECTIVE OPTIMIZATION
INFRASTRUCTURE MAINTENANCE
MODEL
DYNAMICS
REHABILITATION
PREDICTION
NETWORKS
Journal
C
IF:
9.1
Papers:
2.0K
Citations:
10.0K