New tailored reward functions for greedy packing heuristics in the packing-while-travelling problem outperform standard approaches in both deterministic and chance-constrained stochastic settings.
Optimizing monotone chance-constrained submodular functions using evolutionary multiobjective algorithms.Evolutionary Computation, 33(3):363–393
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UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Greedy Approaches for Packing While Travelling with Deterministic and Stochastic Constraints
New tailored reward functions for greedy packing heuristics in the packing-while-travelling problem outperform standard approaches in both deterministic and chance-constrained stochastic settings.
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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.