Deterministic Interpretation of Interval Nonlinear Programming and Its Hierarchical Optimization Solutions
区间非线性规划问题的确定化描述及其递阶求解

We consider a deterministic framework for nonlinear programming involving interval coefficients. A new interpretation of nonlinear programming under uncertainty is proposed by the introduction of the decision making risk coefficient. We present different formulations for controlling the effects of parameter uncertainty present in the objective function or constraints. An algorithm combining Genetic Algorithm (GA) is presented applying the hierarchical optimization structure. The simulation example shows the feasibility of the formulations.