Waste load allocation modeling with fuzzy goals; simulation-optimization approach

Presence of various types of uncertainties in water quality management problems has been recognized as one of the major challenges in water quality modeling. Vagueness, lack of adequate data and nonlinearity of cost and/or benefit functions in most of water quality and waste load allocation management problems have reduced the capability of direct inclusion of uncertainty analysis in the management models. This study presents a fuzzy waste load allocation model in which cost function and the water quality standards or the goals of dischargers and pollution control agencies are expressed with appropriate linear and/or nonlinear and nondecreasing and/or nonincreasing membership functions. QUAL2E and Classified Population Genetic Algorithm (CPGA) were coupled to develop the optimum strategy resulting in maximum value of the minimum nonzero membership values, which represent the optimum satisfaction level of the conflicting goals. Number of constraint violations was used to penalize the fitness function in order to eliminate the infeasible solutions at the final results. The model was applied to a hypothetical case example. Results show a very suitable convergence of the proposed algorithm to good of possibility to the near global optima. Effects of linear and nonlinear membership functions are examined and the results are analyzed.