On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem
Summary: arXiv:2604.13385v1 Announce Type: cross
Abstract
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.
Key Highlights
-
Complexity of Open-Pit Mine Scheduling:
The scheduling involves managing uncertain economic factors and varying resource capacities, requiring robust optimization methods. -
Effectiveness of Evolutionary Algorithms:
These algorithms excel in adapting to fluctuating environments, making them suitable for the dynamic nature of mining operations. -
Dynamic Chance-Constrained Approach:
The proposed method addresses uncertainties in block economic values and variations in mining and processing capacities. -
Bi-Objective Evolutionary Formulation:
The dual focus on profit maximization and standard deviation minimization allows for a balanced approach to risk and reward. -
Diversity-Based Change Response Mechanism:
This innovative mechanism repairs infeasible solutions and generates new feasible ones when changes occur, enhancing adaptability. -
Comparative Analysis:
The study evaluates the new approach against a baseline strategy, showcasing superior performance across various scenarios.
Conclusion
The research presented in this paper significantly contributes to the field of open-pit mine scheduling by effectively integrating evolutionary optimization with dynamic chance constraints.
The results indicate a marked improvement in the scheduling process under uncertain conditions, a common challenge in the mining industry.
By utilizing a bi-objective approach and a diversity-focused mechanism, this study paves the way for more resilient and efficient mining operations, ultimately leading to better resource management and increased profitability.
Future work may explore the application of these methodologies to other areas of resource management and optimization, further enhancing their impact across various industries.