Not availableH.R. Maier, Z. Kapelan, Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Ree

A simulated annealing-based heuristic has been developed to obtain the least-cost design of a looped water distribution network. A Newton search method was used to solve the hydraulic network equations. Simulated annealing is a stochastic optimization method that can work well for large-scale optimization problems that are cast in discrete or combinatorial form, as with the problem proposed. The results obtained with this approach for networks currently appearing in the literature as case studies in this field (whose solution by other optimization methods was known) have proved the ability of the heuristic to handle this kind of problem.

Because the provision of an adequate and reliable water supply system involves a very high level of investment, it is essential that the layout and design of such systems are very carefully planned and the operation of their hydraulic infrastructures is properly managed. This paper describes a simulated annealing method that can be used to find the least-cost design for a gravitational looped water distribution network. Simulated annealing heuristics have proved to be extremely efficient in solving classic combinatorial problems such as the “traveling salesmen” problem. To assess its performance, this heuristic method has been applied to well-known networks in the literature. The results compare very favorably with those obtained by other optimization methods, confirming that simulated annealing is capable of handling nonlinear mixed-integer problems such as this. This encourages further research in this area.

Three optimization models are proposed to select the best subset of stations from a large groundwater monitoring network: (1) one that maximizes spatial accuracy; (2) one that minimizes temporal redundancy; and (3) a model that both maximizes spatial accuracy and minimizes temporal redundancy. The proposed optimization models are solved with simulated annealing, along with an algorithm parametrization using statistical entropy. A synthetic case-study with 32 stations is used to compare results of the proposed models when a subset of 17 stations are to be chosen. The first model tends to distribute the stations evenly in space; the second model clusters stations in areas of higher temporal variability; and results of the third model provide a compromise between the first two, i.e., spatial distributions that are less regular in space, but also less clustered. The inclusion of both temporal and spatial information in the optimization model, as embodied in the third model, contributes to selection of the mos...