Contextual decision-making

Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications

  • Carlos Gamboa
  • Alexandre Street
  • Davi Valladão
  • Bernardo K. Pagnoncelli
    • Categories Energy, Robust Optimization, Stochastic Programming Tags , , ,

    Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. This limitation hampers their use in online decision-making problems such as energy scheduling, where multiperiod optimization must be solved every few minutes. In this paper, we propose a novel Data-Driven Contextual Uncertainty Set, which gives … Read more

    Decision-focused predictions via pessimistic bilevel optimization: complexity and algorithms

  • Victor Bucarey
  • Sophia Calderón
  • Gonzalo Muñoz
  • Frédéric Semet
    • Categories Constrained Nonlinear Optimization, Data Science Applications, Other Topics Tags ,

    Dealing with uncertainty in optimization parameters is an important and longstanding challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic optimization problem is solved. However, the decisions produced by this so-called predict-then-optimize procedure can be highly sensitive to uncertain parameters. In this work, we contribute to recent efforts in producing  decision-focused predictions, i.e., … Read more

    Optimal Robust Policy for Feature-Based Newsvendor

  • Luhao Zhang
  • Jincheng Yang
  • Rui Gao
    • Categories Infinite Dimensional Optimization, Robust Optimization, Supply Chain Management Tags , , ,

    We study policy optimization for the feature-based newsvendor, which seeks an end-to-end policy that renders an explicit mapping from features to ordering decisions. Unlike existing works that restrict the policies to some parametric class which may suffer from sub-optimality (such as affine class) or lack of interpretability (such as neural networks), we aim to optimize … Read more