Group for Research in Decision Analysis

G-2019-89

Adaptive two-stage stochastic optimization of the Escondida mining complex, Chile

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This paper presents the application of adaptive simultaneous stochastic optimization with a representative branching framework to generate the strategic plan of the Escondida mining complex, the world's largest copper-production operation. This adaptive, two-stage stochastic optimization considers geological uncertainty and integrates investment and operational alternatives in the production schedule. Mining complexes are comprised of interconnected components affected by multiple sources of uncertainty. Thus, they must be optimized simultaneously in order to maximize their value and manage risk. Additionally, due to the extensive lives of assets, it is not possible to assume that the current strategic plan will remain optimal. Thus, an operationally feasible method to embed alternatives in the mine plan is used. The method presented provides a strategic plan with representative branches for future possible investment decisions. Adaptive decisions are made sequentially over time, activating costs and effects over the model. The optimizer chooses the optimal strategic production plan accordingly, as well as the investments made and their timing. The Escondida mining complex is a multi-element, multi-pit operation with nine different processing destinations. Investment options considered are increasing truck and shovel fleet, adding a secondary crusher in one of the plants, and investing in a main crusher assigned to one of the pits. Additionally, operational alternatives at the mine and plant levels are included. The adaptive solution shows a substantial probability that the mine plan might change its design substantially due to geological uncertainty, presenting an increased expected NPV compared to the two-stage stochastic formulation.

, 16 pages