Risk Explicit Interval Linear Programming Model for Uncertainty-Based Nutrient-Reduction Optimization for the Lake Qionghai Watershed

摘要: Water quality management is subject to large uncertainties due inherent randomness in the natural system and vagueness decision-making process. For water optimization models, this means that some model coefficients can be represented by probability distributions, while others expressed only ranges. Interval linear programming (ILP) risk explicit interval (REILP) models for optimal load reduction at watershed scale are developed of Lake Qionghai Watershed, China. The solution space an ILP using intervals corresponding lower upper bounds each decision variable. REILP extends through introducing a function aspiration levels ( λpre ) into formulation. able generate practical solutions trade-offs solving series submodels, minimizing under different levels. This illustra...