Robust Optimization

Towards robust optimal control of chromatographic separation processes with controlled flow reversal

  • Dominik H. Cebulla
  • Christian Kirches
  • Andreas Potschka
    • Categories Chemical Engineering, Robust Optimization, Systems governed by Differential Equations Optimization Tags , ,

    Column liquid chromatography is an important technique applied in the production of biopharmaceuticals, specifically for the separation of biological macromolecules such as proteins. When setting up process conditions, it is crucial that the purity of the product is sufficiently high, even in the presence of perturbations in the process conditions, e.g., altered buffer salt concentrations. … Read more

    When Wasserstein DRO Reduces Exactly: Complete Characterization, Projection Equivalence, and Regularization

  • Chenling Huang
  • Jonathan Yu-Meng Li
  • Tiantian Mao
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Wasserstein distributionally robust optimization (DRO), a leading paradigm in data-driven decision-making, entails the evaluation of worst-case risk over a high-dimensional Wasserstein ball–a major computational burden. In this paper, we study when the worst-case risk problem admits an exact reduction to the evaluation of risk over a one-dimensional projected Wasserstein ball—a property we refer to as … Read more

    Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions

  • YoungChul Yoon
  • Grani A. Hanasusanto
  • Yijie Wang
    • Categories Robust Optimization, Stochastic Programming Tags , , , , ,

    Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant challenges when dealing with multimodal or mixtures of distributions. The inherent complexity of such structures often precludes an explicit functional relationship between the contextual information and the uncertain parameters, limiting the direct applicability of parametric … Read more

    Distributionally Robust Universal Classification: Bypassing the Curse of Dimensionality

  • Weijun Xie
    • Categories Robust Optimization, Stochastic Programming Tags , , , ,

    The Universal Classification (UC) problem seeks an optimal classifier from a universal policy space to minimize the expected 0-1 loss, also known as the misclassification risk. However, the conventional empirical risk minimization often leads to overfitting and poor out-of-sample performance. To address this limitation, we introduce the Distributionally Robust Universal Classification (DRUC) formulation, which incorporates … Read more

    Algorithmic Approaches for Identifying the Trade-off between Pessimism and Optimism in a Stochastic Fixed Charge Facility Location Problem

  • Man Yiu Tsang
  • Karmel S. Shehadeh
    • Categories Facility Planning and Design, Robust Optimization, Stochastic Programming Tags , , ,

    We introduce new algorithms to identify the trade-off (TRO) between adopting a distributional belief and hedging against ambiguity when modeling uncertainty in a capacitated fixed charge facility location problem (CFLP). We first formulate a TRO model for the CFLP (TRO-CFLP), which determines the number of facilities to open by minimizing the fixed establishment cost and … Read more

    Mixed-Feature Logistic Regression Robust to Distribution Shifts

  • Qingshi Sun
  • Nathan Justin
  • Andrés Gómez
  • Phebe Vayanos
    • Categories Applications - OR and Management Sciences, Integer Programming, Robust Optimization Tags , , ,

    Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same time, such domains are permeated by distribution shifts, where the distribution generating the data changes between training and deployment. In this paper, we study a distributionally robust logistic regression … Read more

    Inverse Optimization via Learning Feasible Regions

  • Ke Ren
  • Peyman Mohajerin Esfahani
  • Angelos Georghiou
    • Categories (Mixed) Integer Linear Programming, Nonsmooth Optimization, Robust Optimization Tags , , ,

    We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of the literature focuses on learning only the objective function, as learning the constraint function (i.e., feasible regions) leads to nonconvex training programs. Motivated by this, we focus on … Read more

    A data-driven robust approach to a problem of optimal replacement in maintenance

  • Sina Shahri Majarshin
  • Ahmadreza Marandi
  • Geert-Jan van Houtum
    • Categories Applications - OR and Management Sciences, Robust Optimization, Statistics Tags , , ,

    Maintenance strategies are pivotal in ensuring the reliability and performance of critical components within industrial machines and systems. However, accurately determining the optimal replacement time for such components under stress and deterioration remains a complex task due to inherent uncertainties and variability in operating conditions. In this paper, we propose a comprehensive approach based on … Read more

    Data-driven robust menu planning for food services: Reducing food waste by using leftovers

  • Marloes Remijnse
  • Marjolein Buisman
  • Ahmadreza Marandi
  • S.U.K Rohmer
  • Tom Van Woensel
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Robust Optimization Tags , , , ,

    With food waste levels of about 30%, mostly caused by overproduction, reducing food waste poses an important challenge in the food service sector. As food is prepared in advance rather than on demand, there is a significant risk that meals or meal components remain uneaten. Flexible meal planning can promote the reuse of these leftovers … Read more

    On the Semidefinite Representability of Continuous Quadratic Submodular Minimization With Applications to Moment Problems

  • Samuel Burer
  • Karthik Natarajan
    • Categories Quadratic Programming, Robust Optimization, Semi-definite Programming Tags , ,

    We show that continuous quadratic submodular minimization with bounds is solvable in polynomial time using semidefinite programming, and we apply this result to two moment problems arising in distributionally robust optimization and the computation of covariance bounds. Accordingly, this research advances the ongoing study of continuous submodular minimization and opens new application areas therein. ArticleDownload … Read more