Evaluation of Tailored Mutation Operator in a Parallel Genetic Algorithm for Pavement Maintenance Treatment Scheduling

Abstract

The maintenance of an existing large road network is a key focus area for road authorities around the world. The pressures associated with the ever-increasing road network and often shrinking budgets means that it is essential that road authorities invest maintenance budgets wisely. In line with this objective, most road authorities’ employee a Pavement Management System (PMS) to assist in making maintenance decisions. PMSs must solve a very large optimization problem involving thousands of road segments with multiple possible treatments. There is a wide range in the cost of these treatments and also in the magnitude and duration of their improvement. The optimization problem is to identify a minimum cost, 20-year maintenance program that ensures all segments are maintained at an acceptable level (which varies depending on factors such as the amount of traffic and the type of traffic). In addition to the 20-year overall budget, there are yearly budgets constraints which must be met and many other constraints such as the availability of staff and machinery. Previous research has shown significant benefit arises from the adoption of a genetic algorithm-based PMS. This paper builds on this research through the application and evaluation of a tailored, parallel genetic algorithm within a PMS. A tailored genetic algorithm is evaluated using a real-world road network of 1,335 road segments executed using 12 processing units with annual budgets ranging between $40 and $50 million. Over a total of 174 trials, the tailored genetic algorithm was 46% more successful than a standard genetic algorithm at producing an optimised program of works that satisfied all budget constraints, typically with a lower overspend