An integer linear programming approach for pavement maintenance and rehabilitation optimization

ABSTRACT

A highway in poor conditions can raise transportation costs. Due to budgetary constraints, pavement maintenance programming is considered a difficult decision-making problem. In this article we propose a novel mathematical model and a different variant of the pavement maintenance management problem, solved with integer linear programming. The novelty of this approach is the use of the Pavement Surface Rating as the condition indicator, along with a proposed conversion strategy between most used performance indices. Additionally, we propose a simpler and broader deterioration model, when compared to existent ones, using a table system. This renders the model to be solved easily, allowing it to be implemented worldwide, given its generic characteristics. Many computational experiments were performed, both on artificial benchmark instances and on a real-world case study. The proposed model is shown to obtain optimal solutions in short computational times, and it is able to solve much larger instances than the ones found in the literature. Optimal solutions from benchmark instances, consisting of 5,000 segments and an analysis period of 30 years, were found in less than 45 minutes. Additionally, the optimal solutions have a difference of more than 20% in average, when compared to a greedy algorithm.

KEYWORDS:

• Pavement management
• integer linear programming
• network-level maintenance
• optimization model
• pavement maintenance and rehabilitation

Acknowledgments

We thank the contribution of the Brazilian National Department of Transport Infrastructure (DNIT) in providing the pavement condition data of state-controlled federal highways. We also gratefully acknowledge the feedback from anonymous referees, which allowed us to significantly improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 and the Brazilian National Council for Scientific and Technological Development (CNPq). The funding sources had no role in the study design, collection, analysis, or interpretation of the data, writing of the manuscript, or the decision to submit the paper for publication.