Short-team planning for mining operations using robust optimization

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This research program aims to develop and solve a new approach for more robust planning of surface and underground mining operations in the very short-term and short-term. Operational planning determines the mining sequence for a period of three months to one year. It identifies the blocks of material to be mined out during this period and the order in which they will be. This scheduling must take into account several operational constraints such as the precedence among the blocks (among other things, to comply with safety rules on the maximum degree of slope), the precedence among the operational activities (drilling, blasting, transportation), the limit on the amount of ore that can be treated per day, the availability and the location of equipment such as shovels and drills, the access to extraction areas via ramps, etc. Traditionally, deterministic linear programming models have been used for solving operational planning. These models are effective, but they lack of realism because several of the parameters used can not be considered as deterministic: the mechanical and chemical properties of rock, the travel time of trucks, loading time, waiting time at service points, etc. These deterministic models assume accurate values for the system parameters by using a nominal value, i.e. an average value based on geostatistical studies and historical data. In real-world applications, such as those used for short-term and very short-term planning, a uncertainty in the data threatens the relevance of the solution obtained by the deterministic linear programming model in two important aspects: (i) the solution might not actually be feasible when the decision-maker attempts to implement it, and (ii) the solution, when feasible, might lead to a far greater cost (or smaller revenue) than the truly optimal strategy. The research program described in this proposal aims to overcome these drawbacks by proposing a recent approach to modeling and resolution called robust optimization.