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We present a multi-objective, multi-stage stochastic programming with recourse model for reservoir management and operation, where we use utility theory to select the best compromise solution from the Pareto front. A multi-stage streamflow scenario tree is generated first by the neural gas method. Then the Pareto front at each stage is produced by a modified constrained NSGA-II. A single best compromise solution on the Pareto front must be selected for the immediate stage and the model moves forward one stage and is re-optimized over a moving planning horizon of fixed duration. A key contribution of this study is the selection of the best compromise solution, which is achieved by a proposed linear spline utility function allied with regression. Our proposed utility function has the following advantages: 1) it satisfies the law of diminishing marginal rate of substitution, 2) it does not rely on the pre-specified weight or goal, and 3) it selects the best compromise solution that is likely to fall in the “knee regions” of the Pareto front. We apply the proposed optimization model to the Three Gorges Reservoir (TGR) in China. The two conflicting objectives are 1) maximizing the total expected energy output in the planning horizon, and 2) maximizing the average expected ecological benefits in the planning horizon. The results show that the proposed model produces the optimal water release policy successfully under different hydrological scenarios, considering both the inflow uncertainty and the tradeoff between the two conflicting objectives.

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