On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem
Ishara Hewa Pathiranage, Aneta Neumann

TL;DR
This paper presents a bi-objective evolutionary approach with a diversity-based change response mechanism to effectively solve dynamic, uncertain open-pit mine scheduling problems, outperforming baseline strategies.
Contribution
It introduces a novel diversity-based change response mechanism for evolutionary algorithms addressing dynamic, uncertain mine scheduling, improving solution feasibility and quality.
Findings
The proposed method outperforms baseline strategies across multiple instances.
It maintains higher solution diversity during dynamic changes.
The approach is effective under various uncertainty levels and change frequencies.
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…
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