Simulation-optimization approaches for water pump scheduling and pipe replacement problems

Naoum-Sawaya, Joe; Ghaddar, Bissan; Arandia, Ernesto; Eck, Bradley

doi:10.1016/j.ejor.2015.04.028

Cited by 40 publications

(28 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper the authors are concerned with pump scheduling optimization (PSO): which pumps are to be operated and with which settings at different periods of the day, so that the energy cost, the largest operational cost for water utilities, is minimized. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make PSO computationally challenging, even for small WDNs [4]. While mathematical programming approaches linearize/convexify the equations regulating the flow distribution in the WDN, the more recent optimization strategies use a hydraulic simulation software which can solve all the equations and provide computed values relatively to objective function (e.g.…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of pump operations in water distribution systems

2018

View full text Add to dashboard Cite

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an "acquisition" function based on the model, which sets the next observation at the most "promising" point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy.

show abstract

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of pump operations in water distribution systems

2018

View full text Add to dashboard Cite

show abstract

“…Furthermore, these approaches rely on a heuristic algorithm to recover the feasibility of the relaxed solutions in a second phase. As a direct evolution of relaxation techniques, two recent works apply decomposition techniques (lagrangian relaxation [5] and Benders decomposition [26]): to get an optimal solution, the relaxation is solved iteratively driven by a master program. Again, as the convergence is too slow, the method is truncated and coupled with Local Search [5].…”

mentioning

confidence: 99%

A convex mathematical program for pump scheduling in a class of branched water networks

et al. 2017

View full text Add to dashboard Cite

International audienceWe address the day-ahead pump scheduling problem for a class of branched water networks with one pumping station raising water to tanks at different places and levels. This common class is representative of rural drinking water distribution networks, though not exclusively so. Many sophisticated heuristic algorithms have been designed to tackle the challenging general problem. By focusing on a class of networks, we show that a pure model-based approach relying on a tractable mathematical program is pertinent for real-size applications. The practical advantages of this approach are that it produces optimal or near-optimal solutions with performance guarantees in near real-time, and that it is replicable without algorithmic development. We apply the approach to a real drinking water supply system and compare it to the current operational strategy based on historical data. An extensive empirical analysis assesses the financial and practical benefits: (1) it achieves significant savings in terms of operation costs and energy consumption, (2) its robustness to dynamic pricing means that demand-response can be efficiently implemented in this type of energy-intensive utility

show abstract

“…In inventory management problems, for instance, we often need to optimize a system modeled through a discrete event simulation to obtain the optimal stocking quantities ( Jalali & Van Nieuwenhuyse, 2015 ). Examples in other fields include supply chain design ( Saif & Elhedhli, 2015 ), pump scheduling ( Naoum-Sawaya, Ghaddar, Arandia, & Eck, 2015 ), and ambulance fleet allocation ( McCormack & Coates, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

2015

View full text Add to dashboard Cite

a b s t r a c tIn this article we investigate the unconstrained optimization (minimization) of the performance of a system that is modeled through a discrete-event simulation. In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization algorithms (assuming deterministic outputs) to problems with noise. Our objective in this paper is to compare the relative performance of a number of these algorithms on a set of well-known analytical test functions, assuming different patterns of heterogeneous noise. We also apply the algorithms to a popular inventory test problem. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based algorithms to solve engineering and/or business problems, and may be useful in the development of future algorithms.

show abstract

Simulation-optimization approaches for water pump scheduling and pipe replacement problems

Cited by 40 publications

References 30 publications

Bayesian optimization of pump operations in water distribution systems

Bayesian optimization of pump operations in water distribution systems

A convex mathematical program for pump scheduling in a class of branched water networks

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

Contact Info

Product

Resources

About