A matheuristic approach for optimizing mineral value chains under uncertainty

Mineral value chains, also known as mining complexes, involve mining, processing, stockpiling, waste management and transportation activities. Their optimization is typically partitioned into separate stages, considered sequentially. An integrated stochastic optimization of these stages has been shown to increase the net present value of the related mining projects and operations, reduce risk in meeting production targets, and lead to more robust and coordinated schedules. However, it entails solving a larger and more complex stochastic optimization problem than separately optimizing individual components of a mineral value chain does. To tackle this complex optimization problem, a new matheuristic that integrates components from exact algorithms (relaxation and decomposition), machine learning techniques (reinforcement learning and artificial neural networks), and heuristics (local improvement and randomized search) is proposed. A general mathematical formulation that serves as the basis for the proposed methodology is also introduced, and results of computational experiments are presented.