adjustable robust optimization

Adjustable robust optimization for fleet sizing problem in closed-loop supply chains with simultaneous delivery and pickup

  • Ali Borumand
  • Ali Nookabadi
    • Categories (Mixed) Integer Linear Programming, Meta Heuristics, Robust Optimization Tags , , , ,

    The Fleet Sizing Problem (FSP) stands as a critical challenge within the realm of logistics and supply chain management, particularly in the context of Closed-Loop Supply Chains (CLSC). The significance of addressing the FSP lies in its direct impact on operational costs, resource utilization, and environmental sustainability. By effectively optimizing fleet size, organizations can streamline … Read more

    Quadratic Optimization Through the Lens of Adjustable Robust Optimization

  • Abbas Khademi
  • Ahmadreza Marandi
    • Categories Quadratic Programming, Robust Optimization Tags , , , ,

    Quadratic optimization (QO) has been studied extensively in the literature due to its applicability in many practical problems. While practical, it is known that QO problems are generally NP-hard. So, researchers developed many approximation methods to find good solutions. In this paper, we go beyond the norm and analyze QO problems using robust optimization techniques. … Read more

    Information Basis in Dynamic Robust Optimization

  • Ningji Wei
  • Peter Yun Zhang
    • Categories Robust Optimization Tags , , ,

    Dynamic robust optimization deals with sequential, multi-stage decisions in the face of uncertain, worst-case scenarios. To manage its complexity and the curse of dimensionality, decision rules simplify the search for an optimal policy. This paper explores a middle ground between two common decision rules: simple but imprecise constant policies, and accurate but less scalable affine … Read more

    Using Column Generation in Column-and-Constraint Generation for Adjustable Robust Optimization

  • Henri Lefebvre
  • Martin Schmidt
  • Johannes Thürauf
    • Categories (Mixed) Integer Linear Programming, Robust Optimization Tags , , , ,

    Adjustable robust optimization (ARO) is a powerful tool to model problems that have uncertain data and that feature a two-stage decision making process. Computationally, they are often addressed using the column-and-constraint generation (CCG) algorithm introduced by Zhao and Zeng in 2012. While it was empirically shown that the algorithm scales well if all second-stage decisions … Read more

    Neur2RO: Neural Two-Stage Robust Optimization

  • Justin Dumouchelle
  • Esther Julien
  • Jannis Kurtz
  • Elias B. Khalil
    • Categories Robust Optimization Tags , ,

    Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, … Read more

    A robust approach to food aid supply chains

  • Danique de Moor
  • Dick den Hertog
  • H Fleuren
  • Robert Poos
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , , ,

    One of the great challenges in reaching zero hunger is to secure the availability of sufficient nourishment in the worst of times such as humanitarian emergencies. Food aid operations during a humanitarian emergency are typically subject to a high level of uncertainty. In this paper, we develop a novel robust optimization model for food aid … Read more

    Decision-making with Side Information: A Causal Transport Robust Approach

  • Jincheng Yang
  • Luhao Zhang
  • Ningyuan Chen
  • Rui Gao
  • Ming Hu
    • Categories Optimization in Data Science, Robust Optimization, Stochastic Programming Tags , , ,

    We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions. In particular, we propose to consider a distributionally robust formulation based on causal transport distance. Compared with divergence and Wasserstein metric, the causal transport distance is better at capturing the information structure revealed from the conditional distribution … Read more

    Robust Two-Stage Optimization with Covariate Data

  • Bart Paul Gerard Van Parys
  • Amine Bennouna
    • Categories Convex Optimization, Robust Optimization, Stochastic Programming Tags , ,

    We consider a generalization of two-stage decision problems in which the second-stage decision may be a function of a predictive signal but cannot adapt fully to the realized uncertainty. We will show how such problems can be learned from sample data by considering a family of regularized sample average formulations. Furthermore, our regularized data-driven formulations … Read more

    A New Dual-Based Cutting Plane Algorithm for Nonlinear Adjustable Robust Optimization

  • Abbas Khademi
  • Ahmadreza Marandi
  • Majid Soleimani-damaneh
    • Categories Robust Optimization Tags , , , , ,

    This paper explores a class of nonlinear Adjustable Robust Optimization (ARO) problems, containing here-and-now and wait-and-see variables, with uncertainty in the objective function and constraints. By applying Fenchel’s duality on the wait-and-see variables, we obtain an equivalent dual reformulation, which is a nonlinear static robust optimization problem. Using the dual formulation, we provide conditions under … 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