Generative Robust Optimisation defines uncertainty sets via neural network decoders over latent spaces and evaluates them with a five-point framework, validated on planning problems using Wasserstein autoencoders.
An Exact Solution Approach for Portfolio Op- timization Problems Under Stochastic and Integer Constraints
4 Pith papers cite this work. Polarity classification is still indexing.
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MIGP uses integer servings and goal deviations to produce feasible, practical meal plans that outperform post-hoc rounding of continuous solutions in 66% of cases while always succeeding.
The Keplerian TSP models time-dependent interplanetary rendezvous missions as a discrete optimization problem using time-unfolding and ILP solvers, with released benchmarks and heuristics.
MLMC and MLQMC with h- and p-refinement hierarchies achieve significant speedups over standard MC for UQ in cantilever beam problems, with MLQMC showing optimal cost scaling under certain conditions.
citing papers explorer
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h- and p-refined Multilevel Monte Carlo Methods for Uncertainty Quantification in Structural Engineering
MLMC and MLQMC with h- and p-refinement hierarchies achieve significant speedups over standard MC for UQ in cantilever beam problems, with MLQMC showing optimal cost scaling under certain conditions.