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Abstract ▼ AI-Helper

The problem of train scheduling for urban subway lines is a constrained optimization problem of a high complexity with multiple constraints. Our target problem is especially complicated because there are multiple train depots and the numbers of trains leaving out of a certain depot and coming into a...

The problem of train scheduling for urban subway lines is a constrained optimization problem of a high complexity with multiple constraints. Our target problem is especially complicated because there are multiple train depots and the numbers of trains leaving out of a certain depot and coming into another is predetermined. This paper proposes a two-stage search method based on a genetic algorithm for deriving an optimal schedule with minimum daily operational cost satisfying the depot in-out constraint and the others. The first stage focuses on satisfying the most intricate in-out constraint by performing a constrained optimization search with some other constraints deliberately relaxed. The second stage for post-processing conducts some repair operations to satisfy the formerly relaxed constraints followed by another search to derive an optimized schedule with all the constraints satisfied.

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