Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints
This work addresses a domain-specific optimization problem in mining scheduling, presenting an incremental improvement by adapting existing heuristic methods to incorporate chance constraints for uncertainty.
The paper tackles the stockpile blending problem under uncertainty in material grades by introducing chance constraints and proposing a differential evolution algorithm with repair operators to handle complex constraints, resulting in a comparison with deterministic and stochastic models to evaluate different chance constraints.
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.