Current evacuation plans for underground mines are inefficient and outdated. Simulations have proven that smart evacuation can significantly increase the efficiency of escape times in case of an emergency. In a smart evacuation, miners are given real time instructions on a smart device (such as a smart-watch) about their route to a safe haven if an emergency occurs. In order to make smart evacuation possible, an optimization model is needed that determines the most efficient route to safety. Currently, shortest path algorithms, such as Dijkstra’s algorithm, Floyd-Warshall’s algorithm, or ant colony optimization are used for solving the optimization models. These algorithms, however, are computationally inefficient (Dijkstra and Floyd-Warshall) or may not provide the optimal solution (ant colony optimization). In this thesis, a mathematical programming method will be used to calculate the most efficient escape plan. Mathematical programming translates the goal of finding the most efficient escape solution to an optimization problem. The goal of this optimization problem is to minimize the total distance travelled by all individuals that are underground in case of an emergency. This is done by setting an objective function, which sums up all the distances travelled by the miners, and a set of constraints, which are used to localize the miners and safe havens. The ensuing problem statement is solved using the network simplex algorithm. This principle was used to calculate escape solutions in four different scenarios: with or without blocked pathways and with or without correcting the distances for the stamina of the individual miner. Four different types of results were generated: the division of miners among the safe havens, the total distance travelled by all individuals taking part in the evacuation, the path of an individual miner (called Miner X), and the running times of the different scenarios. It was found that blocked paths can have a large impact on the division of miners among safe havens and can significantly increase the total distance travelled. Using stamina categories increases the path length of Miner X (who has a high stamina) and also the running times of the algorithm. It can be concluded that the algorithm works for all four scenarios. As including blocked paths does not give a time penalty, but will locate trapped miners and send their colleagues on safer escape routes, using this feature has no downsides. The usefulness of adding stamina categories to the algorithm can be debated. The running time of the algorithm increases, while the solutions for realistic numbers of miners do not change. Furthermore, there are philosophical and social questions about the ethics of using these types of categories.