# On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem

arXiv: https://arxiv.org/abs/2604.13385 · HTML: https://mlsanity.com/paper/2604.13385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [Pareto Optimization for Subset Selection with Dynamic Cost Constraints](https://mlsanity.com/paper/1811.07806/) (2018)
- [Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms](https://mlsanity.com/paper/2006.11444/) (2020)
- [Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits](https://mlsanity.com/paper/2204.05597/) (2022)
- [3-Objective Pareto Optimization for Problems with Chance Constraints](https://mlsanity.com/paper/2304.08774/) (2023)
- [Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives](https://mlsanity.com/paper/2002.06766/) (2020)
- [Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints](https://mlsanity.com/paper/2102.05303/) (2021)
- [Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem](https://mlsanity.com/paper/2404.06014/) (2024)
- [Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting](https://mlsanity.com/paper/2305.17957/) (2023)
- [Single- and Multi-Objective Evolutionary Algorithms for the Knapsack Problem with Dynamically Changing Constraints](https://mlsanity.com/paper/2004.12574/) (2020)
- [On the Use of Bi-Objective Evolutionary Algorithms for the Stochastic MKP under Dynamic Constraints](https://mlsanity.com/paper/2604.10930/) (2026)
- [Sliding Window Bi-Objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions](https://mlsanity.com/paper/2407.09731/) (2024)
- [Bi-objective chance-constrained evolutionary optimization for large-scale open-pit mine scheduling under geological uncertainty](https://mlsanity.com/paper/2511.08275/) (2025)

## Built on by (most cited first, 1 of 1)

- [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)

Open-pit mine scheduling is a complex real-world optimization problem that involves uncertain economic values and dynamically changing resource capacities. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to uncertain and changing environments. However, uncertainty and dynamic changes are often studied in isolation in real-world problems. In this paper, we study a dynamic chance-constrained open-pit mine scheduling problem in which block economic values are stochastic and mining and processing capacities vary over time. We adopt a bi-objective evolutionary formulation that simultaneously maximizes expected discounted profit and minimizes its standard deviation. To address dynamic changes, we propose a diversity-based change response mechanism that repairs a subset of infeasible solutions and introduces additional feasible solutions whenever a change is detected. We evaluate the effectiveness of this mechanism across four multi-objective evolutionary algorithms and compare it with a baseline re-evaluation-based change-response strategy. Experimental results on six mining instances demonstrate that the proposed approach consistently outperforms the baseline methods across different uncertainty levels and change frequencies.