Hamed Rahimian

A Decomposition Algorithm for Distributionally Robust Chance-Constrained Programs with Polyhedral Ambiguity Set

  • Hamed Rahimian
    • Categories Stochastic Programming Tags , , ,

    In this paper, we study a distributionally robust optimization approach to chance-constrained stochastic programs to hedge against uncertainty in the distributions of the random parameters. We consider a general polyhedral ambiguity set under finite support and study Wasserstein ambiguity set, total variation distance ambiguity set, and moment-based ambiguity set as examples for our computations. We … Read more

    Contextual Stochastic Programs with Expected-Value Constraints

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , ,

    Expected-value-constrained programming (ECP) formulations are a broad class of stochastic programming problems including integrated chance constraints, risk models, and stochastic dominance formulations. Given the wide availability of data, it is common in applications to have independent contextual information associated with the target or dependent random variables of the problem. We show how to incorporate such … Read more

    Distributionally Robust Optimization with Decision-Dependent Polyhedral Ambiguity

  • Hamed Rahimian
  • Sanjay Mehrotra
    • Categories Stochastic Programming Tags , , ,

    We consider a two-stage stochastic program with continuous recourse, where the distribution of the random parameters depends on the decisions. Assuming a finite sample space, we study a distributionally robust approach to this problem, where the decision-dependent distributional ambiguity is modeled with a polyhedral ambiguity set. We consider cases where the recourse function and the … Read more

    Data-Driven Approximation of Contextual Chance-Constrained Stochastic Programs

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , ,

    Uncertainty in classical stochastic programming models is often described solely by independent random parameters, ignoring their dependence on multidimensional features. We describe a novel contextual chance-constrained programming formulation that incorporates features, and argue that solutions that do not take them into account may not be implementable. Our formulation cannot be solved exactly in most cases, … Read more

    Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance

  • Hamed Rahimian
  • Güzin Bayraksan
  • Tito Homem-de-Mello
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    We study multistage distributionally robust optimization (DRO) to hedge against ambiguity in quantifying the underlying uncertainty of a problem. Recognizing that not all the realizations and scenario paths might have an “effect” on the optimal value, we investigate the question of how to define and identify critical scenarios for nested multistage DRO problems. Our analysis … Read more

    Contextual Chance-Constrained Programming

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , , ,

    Uncertainty in classical stochastic programming models is often described solely by independent random parameters, ignoring their dependence on multidimensional features. We describe a novel contextual chance-constrained programming formulation that incorporates features, and argue that solutions that do not take them into account may not be implementable. Our formulation cannot be solved exactly in most cases, … Read more

    A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19

  • Sanjay Mehrotra
  • Hamed Rahimian
  • Masoud Barah
  • Fengqiao Luo
  • Karolina Schantz
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Stochastic Programming Tags , , , , ,

    We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency is to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse … Read more

    A Finitely Convergent Disjunctive Cutting Plane Algorithm for Bilinear Programming

  • Hamed Rahimian
  • Sanjay Mehrotra
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches, Quadratic Programming Tags , , , ,

    \(\) In this paper we present and analyze a finitely-convergent disjunctive cutting plane algorithm to obtain an \(\epsilon\)-optimal solution or detect infeasibility of a general nonconvex continuous bilinear program. While the cutting planes are obtained in a manner similar to Saxena, Bonami, and Lee [Math. Prog. 130: 359–413, 2011] and Fampa and Lee [J. Global … Read more

    Sequential Convexification of a Bilinear Set

  • Hamed Rahimian
  • Sanjay Mehrotra
    • Categories (Mixed) Integer Nonlinear Programming, Quadratic Programming Tags , , , ,

    We present a sequential convexification procedure to derive, in the limit, a set arbitrary close to the convex hull of $\epsilon$-feasible solutions to a general nonconvex continuous bilinear set. Recognizing that bilinear terms can be represented with a finite number nonlinear nonconvex constraints in the lifted matrix space, our procedure performs a sequential convexification with … Read more

    Distributionally Robust Optimization: A Review

  • Hamed Rahimian
  • Sanjay Mehrotra
    • Categories Robust Optimization, Stochastic Programming

    The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. … Read more