Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting

Long-term planning and production scheduling are among the most critical tasks in the area of mining. The goal is to extract valuable ore from an orebody in a sequence that takes into account many mining and precedence constraints in a way that is economically efficient [1]. This is an important real-world optimisation problem that has been studied in the literature over many years. This includes mixed integer programming approaches based on block scheduling [2, 3]. Each block in a block model (a discretised spatial approximation of the mineral deposit) has a given estimated value based on the metal grade and the excavation cost. Other heuristic techniques include dealing with specific characteristics such as uncertainties of the problem [4-6]. Different software products that offer a variety of approaches for mine planning and extraction sequences are available [7, 8]. Evolutionary computation techniques have successfully been applied in the area of mining, in particular to large scale optimisation problems such as the cost efficient extraction of ore [9, 10], the ore processing and blending problem [11-15], and the large-scale open pit mine scheduling problem [16, 17]. Particle swarm algorithms were utilised to solve the capacity constrained open pit mining problem [18] and the transportation and layout problem of locating a crushing station in an open-pit mine [19].