Bibliography

(eng): To detect contaminant intrusion and, in general, to assess quality problems in their water distribution systems, water utilities need quality sensors that continuously measure, directly from the network, conductivity, PH, concentration of different substances, and other related parameters. Due to the nature of the objectives involved, the decision about where to place sensors in the network and the amount of them to be installed is a very challenging problem. In this investigation, we present a multi-objective approach to cast light on those decisions. Instead of a crisp solution, the multi-objective approach will provide a wide spectrum of solutions representing the best trade-off among all the decision criteria of the problem. This approach aims to integrate the practical experience of engineers into the decision-making process since, eventually, the solution will be selected among the Pareto front of solutions using the engineers' experience and the specific characteristics of their utility. To this end, the used algorithm adds agents based on both technical and user-preference rules on top of evolutionary search techniques to explore the decision space. The algorithm runs as a part of the Agent Swarm Optimization framework, a consolidated multi-objective software. Another novelty of this contribution is computational: the evaluation of the objective functions is executed directly in the MS SQL server and simulation data is never required to be loaded in their entirety. Without this important implementation detail, the solution for “large” water network models would not be affordable with the hardware typically used in desktop computers. To illustrate the solution process, a use case focused on a mid-size water supply network is addressed.