Research on preventative rail maintenance to date majors on small or artificial problem instances, not applicable to real-world use cases. This article tackles large, real-world rail maintenance scheduling problems. Maintenance costs and availability of the infrastructure need to be optimized, while adhering to a set of complex constraints. We develop and compare three generic approaches: an evolution strategy, a greedy metaheuristic, and a hybrid of the two. As a case study, we schedule major preventive maintenance of a full year in the complete rail infrastructure of the Netherlands, one of the busiest rail networks of Europe. Empirical results on two real-world datasets show the hybrid approach delivers high-quality schedules.

中文翻译：

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优化铁路年度计划维护

迄今为止，对预防性铁路维护的研究主要针对小型或人为问题实例，不适用于实际用例。本文解决了现实世界中的大型铁路维护调度问题。需要优化基础设施的维护成本和可用性，同时遵守一组复杂的约束条件。我们开发并比较了三种通用方法：进化策略、贪婪元启发式和两者的混合。作为一个案例研究，我们为荷兰的完整铁路基础设施安排了一整年的重大预防性维护，荷兰是欧洲最繁忙的铁路网络之一。两个真实世界数据集的经验结果表明，混合方法提供了高质量的时间表。