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

Author

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

Abstract

Network operation and rehabilitation are major concerns for water utilities due to their impact on providing a reliable and efficient service. Solving the optimization problems that arise in water networks is challenging mainly due to the nonlinearities inherent in the physics and the often binary nature of decisions. In this paper, we consider the operational problem of pump scheduling and the design problem of leaky pipe replacement. New approaches for these problems based on simulation-optimization are proposed as solution methodologies. For the pump scheduling problem, a novel decomposition technique uses solutions from a simulation-based sub-problem to guide the search. For the leaky pipe replacement problem a knapsack-based heuristic is applied. The proposed solution algorithms are tested and detailed results for two networks from the literature are provided.

Suggested Citation

  • Naoum-Sawaya, Joe & Ghaddar, Bissan & Arandia, Ernesto & Eck, Bradley, 2015. "Simulation-optimization approaches for water pump scheduling and pipe replacement problems," European Journal of Operational Research, Elsevier, vol. 246(1), pages 293-306.
    • Handle: RePEc:eee:ejores:v:246:y:2015:i:1:p:293-306
    DOI: 10.1016/j.ejor.2015.04.028
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    References listed on IDEAS

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    1. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    2. D’Ambrosio, Claudia & Lodi, Andrea & Wiese, Sven & Bragalli, Cristiana, 2015. "Mathematical programming techniques in water network optimization," European Journal of Operational Research, Elsevier, vol. 243(3), pages 774-788.
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    4. Ghaddar, Bissan & Naoum-Sawaya, Joe & Kishimoto, Akihiro & Taheri, Nicole & Eck, Bradley, 2015. "A Lagrangian decomposition approach for the pump scheduling problem in water networks," European Journal of Operational Research, Elsevier, vol. 241(2), pages 490-501.
    5. George B. Dantzig, 1957. "Discrete-Variable Extremum Problems," Operations Research, INFORMS, vol. 5(2), pages 266-288, April.
    6. G Mccormick & R S Powell, 2004. "Derivation of near-optimal pump schedules for water distribution by simulated annealing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 728-736, July.
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    Cited by:

    1. Ghaddar, Bissan & Claeys, Mathieu & Mevissen, Martin & Eck, Bradley J., 2017. "Polynomial optimization for water networks: Global solutions for the valve setting problem," European Journal of Operational Research, Elsevier, vol. 261(2), pages 450-459.
    2. Xiaoli Feng & Baoyun Qiu & Yongxing Wang, 2020. "Optimizing Parallel Pumping Station Operations in an Open-Channel Water Transfer System Using an Efficient Hybrid Algorithm," Energies, MDPI, vol. 13(18), pages 1-19, September.
    3. Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
    4. A. Candelieri & R. Perego & F. Archetti, 2018. "Bayesian optimization of pump operations in water distribution systems," Journal of Global Optimization, Springer, vol. 71(1), pages 213-235, May.
    5. Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2016. "Model predictive control strategy of energy-water management in urban households," Applied Energy, Elsevier, vol. 179(C), pages 821-831.
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    7. Bonvin, Gratien & Demassey, Sophie & Le Pape, Claude & Maïzi, Nadia & Mazauric, Vincent & Samperio, Alfredo, 2017. "A convex mathematical program for pump scheduling in a class of branched water networks," Applied Energy, Elsevier, vol. 185(P2), pages 1702-1711.
    8. Selek, István & Ikonen, Enso, 2019. "Role of specific energy in decomposition of time-invariant least-cost reservoir filling problem," European Journal of Operational Research, Elsevier, vol. 272(2), pages 565-573.

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