Optimal pavement maintenance programs based on a hybrid Greedy Randomized Adaptive Search Procedure Algorithm

Abstract

Insufficient investment in the public sector together with inefficient maintenance infrastructure programs lead to higheconomic costs in the long term. Thus, infrastructure managers need practical tools to maximize the Long-Term Effectiveness(LTE) of maintenance programs. This paper describes an optimization tool based on a hybrid Greedy Randomized AdaptiveSearch Procedure (GRASP) considering Threshold Accepting (TA) with relaxed constraints. This tool facilitates the designof optimal maintenance programs subject to budgetary and technical restrictions, exploring the effect of different budgetaryscenarios on the overall network condition. The optimization tool is applied to a case study demonstrating its efficiency to analyzereal data. Optimized maintenance programs are shown to yield LTE 40% higher than the traditional programs based ona reactive strategy. To extend the results obtained in this case study, a set of simulated scenarios, based on the range ofvalues found in the real example, are also optimized. This analysis concludes that this optimization algorithm enhancesthe allocation of maintenance funds over the one obtained under a traditional reactive strategy. The sensitivity analysisof a range of budgetary scenarios indicates that the funding level in the early years is a driving factor of the LTE of optimalmaintenance programs.