Efficient Road Renovation Scheduling under Uncertainty using Lower Bound Pruning

2026-02-17

AI Summary

This study addresses the large-scale road network renewal scheduling problem under uncertainty in infrastructure service life by proposing a novel integrated approach combining machine learning and multi-objective optimization. The problem is formulated as a bilevel multi-objective optimization model, incorporating a probabilistic failure model to capture lifetime uncertainty. To enhance computational efficiency and scalability, a progressive lower-bound pruning strategy is developed, synergistically integrating surrogate models with a multi-objective genetic algorithm. Validation on a large-scale instance comprising 76 projects demonstrates that the proposed method achieves up to a 40-fold improvement in computational efficiency compared to conventional approaches, along with statistically significant improvements across multiple performance metrics.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• road renovation scheduling
• uncertainty
• infrastructure lifespan
• emergency intervention
• traffic congestion

Innovation

• lower bound pruning
• machine learning surrogate models
• multi-objective genetic algorithm
• infrastructure maintenance scheduling
• uncertain deadlines