عنوان مقاله [English]
Optimal capacity estimation of surface reservoir systems with control on reliability of meeting water demands using mathematical programming techniques has been investigated in this study. Having control on the reliability needs the use of binary variables, which makes formulation of the required optimization model a mixed integer linear program (MILP). A hybrid genetic algorithm-linear programming (GA-LP) model has been proposed to deal with solving the MILP models, in which binary variables are the GA decision variables, while the remaining linear programs are solved by standard LP solvers for objective function evaluations. The model, with 420-840 binary variables, has been used in the optimum capacity estimation of the Chergh-Veis dam and compared with branch-and-bound and GA techniques. The results reveal the satisfactory and relative performance of the proposed hybrid algorithm compared to its competitors, in terms of computational speed and quality of solutions. However, in a large-scale problem, generating a huge number of binary variables in GA may pose a significant computational burden on the proposed methodology to guide the generated solutions into the feasible space of the problem.