Efficient Road Renovation Scheduling under Uncertainty using Lower Bound Pruning

TL;DR

This paper presents a hybrid machine learning and genetic algorithm approach to optimize urban road renovation scheduling under uncertain deadlines, significantly improving solution efficiency and quality for large-scale problems.

Contribution

It introduces a progressive lower bound evaluation method that combines surrogate models with genetic algorithms for large-scale infrastructure maintenance under uncertainty.

Findings

Achieves up to 40 times faster computation.

Improves solution quality on large problem instances.

Effectively handles uncertainty in infrastructure lifespans.

Abstract

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.