A semidistributed system dynamics simulation model was coupled with a genetic algorithm to develop a novel simulation–optimization approach for conjunctive water use management. The proposed simulation–optimization method uses the concept of cyclic storage systems as a framework to solve conjunctive use problems. As a highly sophisticated conjunctive use template, a cyclic storage system includes two major subsystems: surface water and groundwater. In this research, the dynamic behavior of a cyclic storage system was simulated using system dynamics methodology. The real‐world case study chosen was Kineh Vars Reservoir and its irrigating area, located downstream of the Abhar Rud watershed in west‐central Iran. Operating rules for the system were optimized to satisfy water demands over a planning period of 40 seasons, minimizing the total costs of system construction, operation, maintenance, and elements replacement.

Abstract-A prototype simulation-optimization system called AnyPLOS, which couples an Artificial Neural Network (ANN) based simulation model with a genetic algorithm optimization model, is presented. AnyPLOS is designed to discover value of effective input parameters of a production line so that all required quality control tests on the output product is satisfied. First an ANN was trained and tested to provide an acceptable level of accuracy in prediction of production line outputs, and then it was coupled with a GA optimization module to find desired solutions. A real world case study, in Erish Khodro manufacturing company, was set up and the foam production process input parameters were optimized so that the produced samples satisfied quality requirements. In order to verify the results, discovered solutions were used to produce real foam samples in the production line. After that, quality control tests were performed on samples. Quality test results were, as predicted by ANN, within the desired range. In order to estimate the performance of the trained ANN, experimental observations were compared to values which were predicted by ANN. A convincing correlation was found between ANN predictions and experimental values.