  Heuristic algorithms have shown a good ability to solve a variety of
optimization problems. Stockpile blending problem as an important component of
the mine scheduling problem is an optimization problem with continuous search
space containing uncertainty in the geologic input data. The objective of the
optimization process is to maximize the total volume of materials of the
operation and subject to resource capacities, chemical processes, and customer
requirements. In this paper, we consider the uncertainty in material grades and
introduce chance constraints that are used to ensure the constraints with high
confidence. To address the stockpile blending problem with chance constraints,
we propose a differential evolution algorithm combining two repair operators
that are used to tackle the two complex constraints. In the experiment section,
we compare the performance of the approach with the deterministic model and
stochastic models by considering different chance constraints and evaluate the
effectiveness of different chance constraints.
