Open-pit mining blast patterns are designed to safely break rock mass at desired boundaries and to produce uniformly sized fragments that lead to the optimal performance of subsequent mining operations. An issue that is often overlooked, however, is the deviation of drillhole locations with respect to their coordinates as designed in the blasting plan. This causes changes in the spatial distribution of explosive energy; some areas of the blast may receive a surplus of explosive energy, while others see a reduction. This results in less uniform fragmentation, producing more fines and increasing chances of oversized boulders, which reduces the efficiency of loading, hauling, and crushing operations. Additionally, deviated drillholes can lead to slope stability issues, flyrock, and uneven benches. To mitigate these problems, this study attempts to reduce differences in the explosive energy distribution (EED) through an optimization approach that adjusts the height of explosive columns in blastholes. A case study combines planned drillhole coordinates of two blast patterns with both simulated deviations and actual deviated drillhole locations. The corresponding benches are discretized into block models, for which the explosive energy distribution is calculated from the planned drillhole configuration as well as the deviated holes. The optimization objective is then to minimize block-to-block differences in explosive energy by making discrete adjustments to the height of individual column charges. Since mathematical programming is not possible due to the complexity of the evaluated formula, solutions are generated by metaheuristic algorithms. Among the Genetic Algorithm (GA) and Tabu Search (TS), the latter is preferred for its local search neighborhood that gives superior computation times. An inconvenience of the block-to-block comparison is the introduction of very large differences in explosive energy in blocks nearest to deviated blastholes, which cannot be accounted for by any charge adjustments except reductions in the deviated hole itself. Because optimization should focus on manageable differences in lower-valued blocks, these high-valued blocks are discarded by applying a cap based on data percentiles. Obtained results show that optimized charging adjustments vary with the chosen percentile, which makes it challenging to define comprehensive truly optimal solutions. A bias for charge reductions at high percentiles gradually shifts towards a bias for charge increases at lower values. Because a bias-induced imbalance in the total amount of explosives (compared to the original charging) is undesirable, the setting closest to equilibrium is selected, which is the use of only the lower 50\% of data. In this configuration, optimization of the case study blast patterns results in improvements of the objective value of 0.53-1.54\%, or 2.14-3.94\% when only the blocks affected by the recommended charge adjustments are considered. The block-to-block comparison handles any interchangement of block values as unwanted differences, which should not always be the case. Therefore, the true improvements to the explosive energy distribution might be better expressed using an alternative optimization objective. Computation times could be further reduced by expanding on the developed algorithm, and combination with prior research would be helpful to include any GPS-related inaccuracies in the actual drillhole locations.

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.