# Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting

arXiv: https://arxiv.org/abs/2305.17957 · HTML: https://mlsanity.com/paper/2305.17957/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 4 of 4)

- [Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits](https://mlsanity.com/paper/2204.05597/) (2022)
- [Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints](https://mlsanity.com/paper/2102.05303/) (2021)
- [A study of the Bienstock-Zuckerberg algorithm, Applications in Mining and Resource Constrained Project Scheduling](https://mlsanity.com/paper/1607.01104/) (2016)
- [Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems](https://mlsanity.com/paper/2104.03440/) (2021)

## Built on by (most cited first, 3 of 3)

- [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)

Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore grades from sparse drilling, leading to large uncertainty in under-sampled parts of the deposit. Planning the extraction schedule of ore over the life of a mine is crucial for its economic viability. We introduce a new approach for determining an "optimal schedule under uncertainty" that provides probabilistic bounds on the profits obtained in each period. This treatment of uncertainty within an economic framework reduces previously difficult-to-use models of variability into actionable insights. The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models. We provide experimental studies using Maptek's mine planning software Evolution. Our results show that our new approach is successful for effectively making use of uncertainty information in the mine planning process.