Abstract: Considering the multiple uncertainties and multi-objectives of water resources systems, we construct a multi-objective optimization allocation model of water resources under multiple uncertainties in the interval, stochastic and fuzzy environments, using comprehensive economic, social and ecological benefits as its objectives, and imposing a water supply capacity constraint and a water demand constraint. We develop a new model solution method featured with a procedure of simulation-based optimization. Monte Carlo simulations are used to generate the input and output data, and a genetic algorithm is adopted to optimize each set of the simulation data to obtain possible optimal solution set for optimal water resources allocation under uncertain conditions and a set of water resources allocation schemes with confidence intervals. This method is validated through a case study of Dalian, Liaoning to obtain reasonable and reliable solutions for its water resources allocation. It shows that the results with probability distribution functions provide richer decision information than traditional methods and help improve decision making on water resources management policies under uncertain environments.

Key words: optimal water resources allocation, stochastic simulation, genetic algorithm, uncertainty, optimal solution set