Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints

TL;DR

This paper introduces a heuristic differential evolution algorithm with repair operators to solve complex stockpile blending problems under uncertainty, ensuring high-confidence constraints and improving optimization outcomes.

Contribution

It presents a novel heuristic approach combining differential evolution and repair operators to handle chance constraints in stockpile blending problems with uncertain data.

Findings

The proposed method outperforms deterministic models in handling uncertainty.

It effectively manages complex constraints with high confidence levels.

The approach demonstrates robustness across different chance constraint settings.

Abstract

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.