Management of Environmental Pollution Control Problems under Stochastic Uncertainty

X. S. Qin
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

This study investigated the applicability of using genetic algorithm (GA) for tackling chance-constrained programming models in environmental management problems. Uncertainties associated with stochastic parameters were projected to the management model through Monte Carlo simulation. This led to the formulation of a chance-constrained model, with both the left- and right-hand sides of constraints being involved with random variables. Two study cases, including air quality management and river water pollution control, was used to demonstrate the applicability of the proposed method. Both cases had a need for seeking cost-effective management plans under uncertainty. In many environmental pollution control management problems, uncertainty may exist in related costs, impact factors, and objectives, and influence the system behavior. It is therefore desired that a more efficient method for tackling chanceconstraint uncertainties be proposed for aiding solution of management of environmental pollution control problems. The objective of this study is to explore such a method based on genetic algorithm (GA), and apply it to both air quality management and river water quality control cases.

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