Boosting Genetic Algorithm Performance in Pump Scheduling Problems with a Novel Decision-Variable Representation
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VIEW THE REPLYPublication: Journal of Water Resources Planning and Management
Volume 146, Issue 5
Abstract
Pumping stations used in water distribution networks (WDNs) consume a significant portion of the energy required to deliver municipal drinking water. Smart management strategies such as optimal pump scheduling (OPS) have gained the attention of water companies and managing authorities because they help reduce both energy costs and detrimental consequences for the environment. Genetic algorithms (GAs) are frequently used to approximate the solution of OPS problems, although many researchers have resorted to hybrid models to improve computational performance. This paper shows that despite the lack of support in the literature, a well-designed GA is capable of tackling OPS problems effortlessly. In addition, a new decision-variable representation is proposed, specifically suited to parallel pump systems, which is able to further improve the performance of a GA. Finally, the outperforming capabilities of the new variable representation are demonstrated with two case studies from recent literature.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The work of the second author, Andrea D’Aniello, was financially supported by the fund “PON Ricerca e Innovazione 2014–2020, Asse I, Investimenti in Capitale Umano, Avviso AIM – Attrazione e Mobilità Internazionale, Linea 1” (CUP E61G18000530007).
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Journal of Water Resources Planning and Management
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: May 6, 2019
Accepted: Nov 15, 2019
Published online: Mar 2, 2020
Published in print: May 1, 2020
Discussion open until: Aug 2, 2020
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