A diversity-based change response mechanism in bi-objective evolutionary algorithms improves handling of dynamic chance-constrained open-pit mine scheduling compared to baseline re-evaluation.
Chance-constrained programming.Management science, 6(1):73–79
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Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.
citing papers explorer
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On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem
A diversity-based change response mechanism in bi-objective evolutionary algorithms improves handling of dynamic chance-constrained open-pit mine scheduling compared to baseline re-evaluation.
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On the Use of Bi-Objective Evolutionary Algorithms for the Stochastic MKP under Dynamic Constraints
Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.