Bounds and convex heuristics for bi-objective optimal experiment design in water networks

Optimal Experiment Design for parameter estimation in water networks has been traditionally formulated to maximize either hydraulic model accuracy or spatial coverage. Because a unique sensor configuration that optimizes both objectives may not exist, these approaches inevitably result sub-optimal configurations with respect one of the objectives. This paper presents new bi-objective optimization problem formulation investigate trade-offs between conflicting We develop convex heuristic approximate Pareto front, and compute guaranteed bounds discard portions criterion space do contain non-dominated solutions. Our method relies on Chebyshev scalarization scheme optimization. implement proposed methods optimal experiment design an operational network from UK. For this case study, computes near-optimal solutions individual objective minimization problems, tight true front considered problem.