Identification of the Best Booster Station Network for a Water Distribution System
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 5
Abstract
A fuzzy decision-making framework (DMF) is combined with a hybrid genetic algorithm–linear programming (GA-LP) optimization approach to determine the best booster station network for a water distribution system. The proposed hybrid GA-LP model simultaneously optimizes two conflicting objectives; namely, minimization of total chlorine injection dosage and the number of booster stations. At the same time, residual chlorine concentrations are kept within desired limits. Adjustment of the relative importance of two conflicting objectives results in different optimal solutions. Selection of the best alternative among these optimal solutions is performed through a fuzzy multiobjective DMF. The proposed DMF allows incorporation of the decision makers’ preferences into the booster station network design. In this study, three fuzzy objectives are selected based on economic, operational, and health-related concerns. The hybrid GA-LP model is applied to a case study, and results show that the proposed methodology is effective in maintaining chlorine residuals within desired limits networkwide while minimizing the total chlorine injection, and the fuzzy DMF is a useful tool for incorporating the case specific limitations into the decision process.
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Acknowledgments
This study is based on work supported by The Turkish Academy of Sciences (TÜBA)—The Young Scientists Award Programme (GEBIP). The first author would like to thank TÜBA for their support of this study. The authors also would like to thank Dr. Sinan Turhan Erdoğan for his proofreading and suggestions. Finally, the constructive reviews provided by two anonymous reviewers, editor, and associate editor are greatly appreciated.
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Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 5 • May 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jan 13, 2014
Accepted: Jun 23, 2014
Published online: Aug 7, 2014
Discussion open until: Jan 7, 2015
Published in print: May 1, 2015
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