The simulation as a stand alone optimization tool of a complex system such as a vertical integrated mining operation, significantly over simplifies the actual picture of the system processes involved resulting in an unaccountable effort and resources being spent on optimizing Non Value Added (NA) processes. This study purposed to develop a discrete stochastic simulation-optimization model to accurately capture the dynamics of the system and to provide a structured way to optimize the Value Added (VA) processes. The mine operation model to be simulated for this study is designed as a hybrid level throughput model to identify the VA processes in a mining operation. This study also allows a better understanding of the impact of variation on the likelihood of achieving any given overall result. The proposed discrete stochastic simulation- optimization model provides the ability for a process manager to gain realistic understanding of what a process can do if some factors constraining the process were to be optimized i.e. to conduct what-if analysis. Another benefit of this approached technique is to be able to estimate dependable and reasonable returns on a large optimization related expenditure. The inputs into the model are the capability of the processes which are entered using various variables depending on how much information is available; simple inputs for least amount of information to detailed inputs for well known process to combinational inputs for somewhere in between. The process bottlenecks are identified and measured using the outputs of the model which include production output, severity of constraints, capacity constraints and cumulative bottleneck plots. Once a base case has been identified and documented then the inputs can be modified to represent the business initiatives and the outputs can be compared to the base case to evaluate the true value of the initiative.