Mineral value chains or mining complexes involve mining, processing, stockpiling, waste management, and transportation activities. An integrated stochastic optimization of all related aspects and activities has ben shown to increase the net present value of related mining projects and operations, reduce risk in meeting production targets, and lead to more robust and coordinated production schedules. However, it entails solving a substantially large and more complex stochastic optimization problem than separately optimizing individual components of a mineral value chain. 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.