Using Evolutionary Optimization Techniques for Scheduling Water Pipe Renewal Considering a Short Planning Horizon

: The maintenance and management of underground infrastructures is a growing problem for a majority of municipalities. The maintenance costs are increasing while the financial resources of municipalities remain limited. Water distribution system (WDS) managers therefore need tools to assist them in the elaboration of pipe renewal management plans. In this article, results of a newly developed strategy for pipe renewal based on a cost function are presented. The strategy allows the minimization of a cost function while also considering hydraulic criterion. This strategy was tested on a short planning horizon of five years. The pipe number to be replaced and the optimal moment for renewal are identified using three different optimization techniques: IGA (Island Genetic Algorithm), NPGA‐2 (Niched Pareto Genetic Algorithm 2), and NSGA‐II (Non‐dominated Sorting Genetic Algorithm‐II). The proposed approach has five distinctive features: (1) it is coupled with a flexible evolutionary framework that allows the user to select any type of operator for IGA or any kind of multiobjective genetic algorithm; (2) it uses the hydraulic simulator Epanet2.0 which allows steady state or dynamic simulations; (3) it considers a probabilistic break model to evaluate the structural deterioration of pipes; (4) it integrates a Bayesian approach for the estimation of the pipe break model parameters that take into account the influence of inherent uncertainties related to the quality of data during the decision‐making process; and (5) it simulates the variation of the pipe's roughness over the years. The developed strategy/model is explained using an example that allows us to elucidate its most important components. Simulation experiments on a small network (100 pipes) are presented. A comparison of three evolutionary algorithm results is provided. Tests showed that IGA performs well, but for networks of larger sizes, we recommend increasing the number of demes to reach better solutions. Higher quality results were achieved with NSGA‐II than NPGA‐2 on differently sized networks. We recommend the use the NSGA‐II to optimize large WDS. Future developments for this strategy are also discussed.