What question did this study set out to answer?

To optimize the ore-drawing process by addressing cost, risk, recovery, and dilution under uncertainty using the OGOOSE algorithm.

February 2, 2026SymmetryOpen Access

Robust Multi-Objective Optimization of Ore-Drawing Process Using the OGOOSE Algorithm Under an ε-Constraint Framework

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This analysis demonstrates improved optimization of cost and risk in sublevel caving, suggesting a robust framework for mining operations.

To optimize the ore-drawing process by addressing cost, risk, recovery, and dilution under uncertainty using the OGOOSE algorithm.
Developed the OGOOSE algorithm with three mechanisms: Opposition-Based Learning, Adaptive Inertia Weight, Boundary Reflection Mechanism.
Utilized an ellipsoid-plane geometric surrogate for risk modeling.
Applied the ε-constraint method to decompose the four-objective optimization problem.
Achieved the lowest Friedman rank on CEC2017 benchmarks compared to GOOSE, WOA, and HHO.
Demonstrated a 28.95% average dilution rate, the lowest among comparators, without increasing costs.
Identified an optimal 'knee-point' for managing risk control and dilution limits through sensitivity analysis.

Cai et al. (2026) studied this question.

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This analysis demonstrates improved optimization of cost and risk in sublevel caving, suggesting a robust framework for mining operations.