A methodology for reflecting stochastic considerations in an optimization model is presented. The technique, which uses chance-constrained programming, is applied to a water quality management problem wherein concern is with the interaction between biochemical oxygen demand (BOD) and the dissolved oxygen (DO) concentration in a river. The uncertainty in the problem is considered to be embodied in transfer coefficients for which a lognormal distribution is derived from moment estimates provided by first-order uncertainty analysis. The appropriateness of the lognormal distribution is confirmed by results from a simulation modelling exercise.
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Burn et al. (1986) studied this question.
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