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|This paper attempts to model long-term production scheduling problems by chance constrained binary integer programming in a stochastic environment. This stochastic model is set up to account for ore block grade uncertainty. The probability distribution function of grade in each block is used as a stochastic input to the optimization model. This distribution function in each block should be determined using a Geostatistical Simulation approach. The deterministic equivalents of these chance constraints are then achieved, which are in the form of nonlinear in binary variables. A confidence level at which it is desired that the uncertain constraints holds, is specified in each scheduling period. Rather than the previous risk-based model, this formulation will yield schedules with a high chance of achieving planned production targets while maximizes the expectation of net present value and minimizing the variance function simultaneously. Using this method, the grade uncertainty is integrated explicitly into the optimization process.|