development of reservoir operation policies using integrated optimization-simulation approach

this study is focused on developing an integrated optimization-simulation based genetic algorithm model (iosga) to develop the operational policies for a multi-purpose reservoir system. the objective function of the optimization model is considered to be a linear function of reliability (rel), resiliency (res), and vulnerability (vul) of the river-reservoir system. genetic algorithm (ga) is employed to solve the optimization model in which the coefficients of reservoir operation policy equations are considered as decision variables. these coefficients are formulated in the form of fuzzy numbers to be able to capture the variations in releases and in water demands. due to significant variations of agricultural water demands during different months and years, a water demand time series is considered as one of the inputs of the optimization model. zayandeh-rud river-reservoir system, in central part of iran, is considered the case study. the results of the proposed approach are compared with those of the classic three cyclic algorithm in which the reservoir releases are the decision variables of the optimization model and the iosga model in which the coefficients of reservoir operation policies are considered to be classic (non-fuzzy) numbers. the results of the study indicated that the developed algorithm can significantly reduce the time and costs of modeling efforts and the run-time of the ga model, while it has also improved the overall performance of the system in terms of rel, res, and maximum vulnerability (vulmax) and the coefficient of efficiency (ce) and standard error (se).