Designing an optimal water quality monitoring network for river systems using constrained discrete optimization via simulation

Abstract

The problem of designing a water quality monitoring network for river systems is to find the optimal location of a finite number of monitoring devices that minimizes the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic discrete optimization via simulation (OvS) problem on the expected detection time with a stochastic constraint on detection reliability; and it is solved with an OvS algorithm combined with a recently proposed method called penalty function with memory (PFM). The performance of the algorithm is tested on the Altamaha River and compared with that of a genetic algorithm due to Telci, Nam, Guan and Aral (2009).

Keywords:

• discrete optimization via simulation
• stochastic constraints
• penalty function with memory
• river system
• water quality

Acknowledgements

This work is supported by NSF grant number CMMI-0644837.