To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.

为了满足其主要目标，即为消费者提供优质的水，需要对供水网络（WDN）进行适当的监控。只有设计良好且可靠的监视数据才能使WDN管理员在其系统上做出合理的决策。出于这种信念，全世界的自来水公司已经在监视和数据采集系统上进行了投资。但是，良好的监控需要最佳的传感器放置，并且会带来多目标问题，其中成本和质量是相互矛盾的目标（以及其他）。在本文中，我们通过将使用EPANET-MSX的质量模拟与两种优化技术相集成，解决了该多目标问题的解决方案。首先，使用多目标优化来建立非支配解决方案的帕累托前沿，该方案将污染检测时间和检测概率与成本联系起来。为了帮助决策者选择能够为他们的效用提供最佳折衷的最优解决方案，然后使用具有双重目标的多准则决策技术：1）根据网络敏感性和熵将Pareto解决方案聚类作为评估参数；2）在考虑公用事业角度时，对每个集群中的解决方案进行排名，以提供对问题的更深入了解。集群过程考虑与供水实用需求和可用信息有关的特征，可帮助决策者从帕累托选择可靠且有用的解决方案面前。因此，尽管有关传感器放置的一些工作停止在多目标优化上，但这项工作又向前迈进了一步，并提供了减少和简化的Pareto前沿，其中突出了最佳解决方案。所提出的方法使用NSGA-II算法来解决优化问题，并通过ELECTRE TRI进行聚类。所开发的方法应用于非常著名的基准WDN，为此显示了该方法的实用性。与四个最佳解决方案集群相对应的最终结果对于质量传感器网络上的项目规划和开发过程中的决策者很有用。所获得的集群具有鲜明的特征，为最终项目确定了最方便的解决方案的优先次序提供了开路，并保证了实施帕累托最优解决方案。对此方法的有效性进行了说明。与四个最佳解决方案集群相对应的最终结果对于质量传感器网络上的项目规划和开发过程中的决策者很有用。所获得的集群具有鲜明的特征，为最终项目确定了最方便的解决方案的优先次序提供了开路，并保证了实施帕累托最优解决方案。对此方法的有效性进行了说明。与四个最佳解决方案集群相对应的最终结果对于质量传感器网络上的项目规划和开发过程中的决策者很有用。所获得的集群具有鲜明的特征，为最终项目确定了最方便的解决方案的优先次序提供了开路，并保证了实施帕累托最优解决方案。