Generative Robust Optimisation

• URL: http://arxiv.org/abs/2606.22536v1
• Date: Sun, 21 Jun 2026 14:50:40 GMT
• Title: Generative Robust Optimisation
• Abstract summary: We propose a framework in which a deep generative model defines the uncertainty set as the image of a neural network decoder.<n>We instantiate this framework with a Wasserstein Adrial Autoencoder employing Gaussian mixture model-guided training for latent regularity and constraint-consistency regularisation.
• Score: 4.038424288747438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classical uncertainty sets for robust optimisation impose fixed geometric shapes that cannot represent the complex dependencies present in real-world data. We propose Generative Robust Optimisation (GRO), a framework in which a deep generative model defines the uncertainty set as the image of a neural network decoder over a calibrated latent set, naturally accommodating nonlinear correlations, asymmetry, and multimodality. A five-point evaluation framework (reconstruction fidelity, distribution matching, latent regularity, robust relevance, and computational tractability) provides systematic, model-agnostic criteria for assessing any neural network-based uncertainty set. We instantiate this framework with a Wasserstein Adversarial Autoencoder employing Gaussian mixture model-guided training for latent regularity and constraint-consistency regularisation for robust relevance. Restricting the decoder to ReLU activations enables exact worst-case verification through mixed-integer programming embedding. Extensive experiments on a production planning problem across six uncertainty distributions and six generative architectures, together with a multi-period facility location study, validate the framework and demonstrate that systematic attention to all five criteria yields uncertainty sets that are simultaneously expressive, well-calibrated, and optimisation-tractable.

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