Abstract
This paper develops a general formulation of dependent-chance goal programming (DCGP) which is an extension of stochastic goal programming in a complex stochastic system, and gives an example of water allocation and supply to show the application of DCGP. A genetic algorithm based approach is also presented to solve such a model. DCGP is available to the systems in which there are multiple stochastic inputs and multiple outputs with their own reliability levels. The characteristic of DCGP is that the chances of some probabilistic goals are Dependent, i.e., the goals cannot be considered in isolation or converted to their deterministic equivalents. Finally, Monte Carlo simulation is also discussed for calculating the chance functions in complex stochastic constraints.
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