Chang, Li-ChiuLi-ChiuChangChang, Fi-JohnFi-JohnChangWang, Kuo-WeiKuo-WeiWangDai, Shin-YiShin-YiDai2013-08-162018-06-292013-08-162018-06-292010-08http://ntur.lib.ntu.edu.tw//handle/246246/258228To derive an optimal strategy for reservoir operations to assist the decision-making process, we propose a methodology that incorporates the constrained genetic algorithm (CGA) where the ecological base flow requirements are considered as constraints to water release of reservoir operation when optimizing the 10-day reservoir storage. Furthermore, a number of penalty functions designed for different types of constraints are integrated into reservoir operational objectives to form the fitness function. To validate the applicability of this proposed methodology for reservoir operations, the Shih-Men Reservoir and its downstream water demands are used as a case study. By implementing the proposed CGA in optimizing the operational performance of the Shih-Men Reservoir for the last 20 years, we find this method provides much better performance in terms of a small generalized shortage index (GSI) for human water demands and greater ecological base flows for most of the years than historical operations do. We demonstrate the CGA approach can significantly improve the efficiency and effectiveness of water supply capability to both human and ecological base flow requirements and thus optimize reservoir operations for multiple water users. The CGA can be a powerful tool in searching for the optimal strategy for multi-use reservoir operations in water resources management.To derive an optimal strategy for reservoir operations to assist the decision-making process, we propose a methodology that incorporates the constrained genetic algorithm (CGA) where the ecological base flow requirements are considered as constraints to water release of reservoir operation when optimizing the 10-day reservoir storage. Furthermore, a number of penalty functions designed for different types of constraints are integrated into reservoir operational objectives to form the fitness function. To validate the applicability of this proposed methodology for reservoir operations, the Shih-Men Reservoir and its downstream water demands are used as a case study. By implementing the proposed CGA in optimizing the operational performance of the Shih-Men Reservoir for the last 20. years, we find this method provides much better performance in terms of a small generalized shortage index (GSI) for human water demands and greater ecological base flows for most of the years than historical operations do. We demonstrate the CGA approach can significantly improve the efficiency and effectiveness of water supply capability to both human and ecological base flow requirements and thus optimize reservoir operations for multiple water users. The CGA can be a powerful tool in searching for the optimal strategy for multi-use reservoir operations in water resources management. © 2010 Elsevier B.V.716813 bytesapplication/pdfen-USReservoir operationConstrained genetic algorithms (CGA)Penalty strategyEcological base flowConstrained genetic algorithms (CGA); Ecological base flow; Penalty strategy; Reservoir operation[SDGs]SDG6Baseflows; Decision making process; Fitness functions; Operational performance; Optimal strategies; Penalty function; Penalty strategy; Reservoir operation; Reservoir storage; Shih-Men reservoir; Water demand; Water release; Water resources management; Water users; Aerodynamics; Decision making; Ecology; Function evaluation; Genetic algorithms; Groundwater flow; Optimal systems; Optimization; Reservoir management; Water resources; Water supply; Reservoirs (water); baseflow; decision making; genetic algorithm; index method; reservoir; water management; water resourcejournal article10.1016/j.jhydrol.2010.06.031http://ntur.lib.ntu.edu.tw/bitstream/246246/258228/2/Constrained Genetic Algorithms for Optimizing Multi-use Reservoir Operation.pdf