Identification of the Best Booster Station Network for a Water Distribution System

Abstract

AbstractA 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 method...