Abstract
This paper further discusses the techniques of dependent-chance programming, dependent-chance multiobjective programming and dependent-chance goal programming. Some illustrative examples are provided to show how to model complex stochastic decision systems by using dependent-chance programming and how to solve these models by employing a Monte Carlo simulation based genetic algorithm.
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