Adaptive policies for short-term material flow optimization in a mining complex

Abstract

With the advent of inexpensive sensors and digital storage, increasing amounts of data about a mining complex can be collected. This data can, in turn, be used to continuously adapt stochastic optimization models and short-term mining decision making, thus reducing and managing local uncertainty. Taking advantage of this uncertainty reduction requires new decisionmaking approaches, mechanisms, and optimization methods. This paper proposes the use of state-dependent policies, which encode recipes for responding to new information as it comes along. Focusing on short-term planning shows how to represent and optimize state-dependent policies for making adaptive destination decisions for materials mined and processed. Resulting policies can be applied across different short-term time scales, marking an important step towards simultaneously optimizing different time scales. An implementation of the proposed method at a copper-gold deposit shows that it can improve the utilization of processing streams, production and financial performance over simple heuristic approaches and practices.