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

arXiv: https://arxiv.org/abs/2102.05303 · HTML: https://mlsanity.com/paper/2102.05303/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting](https://mlsanity.com/paper/2305.17957/) (2023)
- [Bi-objective chance-constrained evolutionary optimization for large-scale open-pit mine scheduling under geological uncertainty](https://mlsanity.com/paper/2511.08275/) (2025)
- [On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem](https://mlsanity.com/paper/2604.13385/) (2026)
- [Adaptive Operator Selection in Bilevel Large Neighborhood Search for Electric Autonomous Dial-a-Ride Problem under Uncertainty](https://mlsanity.com/paper/2610.04219/) (2026)

## Abstract (arXiv)

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.