Abstract
The restoration and remodeling of the urban water supply system are traditional challenges for water companies due to either aged existing water supply networks or lodging expansion. These challenges involve the uncertainties induced by their lengthy-planned prospects and the impossible exact prediction of forthcoming events. In this regard, correlations exacerbate unpredictable data and parameters and probably undermine taking effective decisions in this context. Therefore, the remodel and restoration decision of water supply systems must be made using approaches that can effectively deal with correlation uncertainties. The present study develops a bi-objective stochastic optimization model that can handle interrelated uncertain parameters in the water supply system remodeling and restoration issue. The proposed mathematical model is validated using the data of the Mashhad Plain water supply system as a real case study, followed by performing and comparing different levels of conservatism and reliability. As a complex optimization problem, an efficient algorithm is needed to solve the problem. To this end, a hybrid meta-heuristic algorithm, which is a combination of the Red Deer Algorithm (as a newly introduced nature-inspired heuristic) and Simulated Annealing (as a traditional local search algorithm), is proposed. Considering the advantages of these algorithms, it is possible to alleviate the disadvantages of current methods when solving large-scale networks. Finally, an extensive comparison and discussion are made and then the main findings with practical solutions are presented to significantly evaluate the proposed model and algorithm.
Highlights
INTRODUCTIONMunicipal Water Supply System (MWSS) provides the designs of physical infrastructures for water purification from different high-grade water supplies (such as dams and aquifers) and water delivery to several demand sites in the urban areas [1], [2]
As it is evident from all plots, our red deer algorithm (RDA)-simulated annealing (SA) is highly efficient in comparison with non-dominated sorted genetic algorithm-II (NSGA-II) in all criteria
The whole cost of remodeling and the quantity of water wastage via seepage were taken as performance parameters for optimization of the relevant network designing
Summary
Municipal Water Supply System (MWSS) provides the designs of physical infrastructures for water purification from different high-grade water supplies (such as dams and aquifers) and water delivery to several demand sites in the urban areas [1], [2]. They proposed a new optimization modeling approach consisting of a Multi-Level Multi-Objective Stochastic Programming (MLMOSP) and used the weight quantification method to formulate the sustainable water allocation of that area These authors investigated four objectives including the number of key factors affecting water allocation systems, describing the main conflicting goals at each decision-making level, considering exchanges between conflicting goals, and reflecting the leader relationships following different scenarios of surface water accessibility. The current research was performed using an optimization simulation model for continual management of groundwater usage to achieve two main objectives: (1) minimizing deficiencies in meeting irrigation needs and 2) maximizing total net agricultural profit for the main crops of an agricultural sector To achieve these main goals, the Genetic Programming (GP) method was first used to simulate the interactions of water and groundwater levels.
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