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Existing techniques for the cost optimization of water distribution networkseither employ meta-heuristics, or try to develop problem-specific optimizationtechniques. Instead, we exploit recent advances in generic NLP solvers andexplore a rich set of model refinement techniques. The networks that we studycontain a single source and multiple demand nodes with residual pressureconstraints. Indeterminism of flow values and flow direction in the networkleads to non-linearity in these constraints making the optimization problemnon-convex. While the physical network is cyclic, flow through the network isnecessarily acyclic and thus enforces an acyclic orientation. We devisedifferent strategies of finding acyclic orientations and explore the benefit ofenforcing such orientations explicitly as a constraint. Finally, we propose aparallel link formulation that models flow in each link as two separate flowswith opposing directions. This allows us to tackle numerical difficulties inoptimization when flow in a link is near zero. We find that all our proposedformulations give results at par with least cost solutions obtained in theliterature on benchmark networks. We also introduce a suite of large testnetworks since existing benchmark networks are small in size, and find that theparallel link approach outperforms all other approaches on these biggernetworks, resulting in a more tractable technique of cost optimization.

Cost Optimization of Water Distribution Networks: Model Refinement Is Better Than Problem-Specific Solving Techniques