Urban infrastructure degrades over time, necessitating periodic renovation to maintain functionality and safety. When renovation is delayed beyond the infrastructure's remaining lifespan, costly emergency interventions become necessary to prevent failure. Decision makers must therefore balance expected emergency intervention costs against traffic congestion impacts. We formalize this trade-off as a road network maintenance scheduling problem with uncertain deadlines, which presents optimization challenges including computationally expensive evaluation and an exponentially growing solution space. To address these challenges, this paper contributes a hybrid optimization approach combining machine learning with genetic algorithms for large-scale infrastructure renovation scheduling under uncertainty. We formulate the problem as a bi-level multi-objective optimization problem that explicitly accounts for uncertain infrastructure lifespans through probabilistic failure models. We develop a progressive lower bound evaluation method that integrates machine learning surrogate models with a multi-objective genetic algorithm to improve solution quality by enabling more iterations within fixed computational budgets. We demonstrate the method's effectiveness on substantially larger problem instances (76 projects) than previously addressed in the literature, achieving statistically significant improvements across multiple performance metrics by increasing computational efficiency up to 40 times compared to standard approaches.

翻译：城市基础设施会随时间退化，需要定期翻新以维持功能与安全性。当翻新延迟超过基础设施的剩余使用寿命时，为避免失效必须采取代价高昂的紧急干预措施。因此，决策者必须在预期紧急干预成本与交通拥堵影响之间取得平衡。我们将这一权衡关系形式化为具有不确定截止期限的道路网络维护调度问题，该问题存在计算评估成本高昂和解空间指数级增长等优化挑战。为应对这些挑战，本文提出一种混合优化方法，将机器学习与遗传算法相结合，用于不确定性条件下的大规模基础设施翻新调度。我们将该问题构建为双层多目标优化问题，通过概率失效模型显式考虑基础设施寿命的不确定性。我们开发了一种渐进式下界评估方法，将机器学习代理模型与多目标遗传算法相结合，通过在固定计算预算内实现更多迭代次数来提升解的质量。我们在比文献中以往研究规模显著更大的问题实例（76个项目）上验证了该方法的有效性，相比标准方法将计算效率提升高达40倍，在多项性能指标上均取得了统计学显著的改进。