Using Stochastic Binary Integer Programming Approach for Long-Term Production Planning in Open Pit Mines

In this paper long-term production planning of an open pit mine was performed by using chance constrained binary integer programming in a stochastic environment. At first, long-term production planning of an open pit mine was model as a linear zero-one model. In this model block ore grade is an uncertain parameter. 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 Geostatistical Simulation approach. The deterministic equivalents of these chance constraints are then achieved which are the form of nonlinear in binary variables. A confidence level at which it is desire that the uncertain constraints holds, is specified in each scheduling period. Because of difficulties in solving large scale nonlinear models, the model is approximated by a linear one. Rather than previous risk-based model, this formulation will yield schedules with high chance of achieving planned production targets while maximizes the expectation of net present value and minimizes the variance function simultaneously; therefore, using this method the grade uncertainty is integrated explicitly into optimization process