On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem

2026-04-14

AI Summary

This study addresses the dual challenge of economic uncertainty and dynamically varying resource capacities in open-pit mine scheduling by proposing a bi-objective evolutionary optimization framework that simultaneously maximizes expected discounted profit and minimizes its standard deviation. The work uniquely integrates uncertain parameters and time-varying constraints within a unified modeling approach, introducing a novel diversity-driven change-response mechanism that combines chance-constrained programming, multi-objective evolutionary algorithms, and repair-and-replenishment strategies tailored for dynamic environments. Experimental results across six real-world mine instances demonstrate that the proposed method consistently outperforms re-evaluation-based baseline strategies under varying levels of uncertainty and constraint change frequencies.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Evolutionary Computation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• open-pit mine scheduling
• dynamic optimization
• chance-constrained
• uncertainty
• resource capacity

Innovation

• dynamic optimization
• chance-constrained programming
• evolutionary algorithms
• mine scheduling
• uncertainty handling