Chance-Constrained Optimization under Limited Distributional Information: A Review of Reformulations Based on Sampling and Distributional Robustness

  • Simge Küçükyavuz
  • Ruiwei Jiang
    • Categories Stochastic Programming Tags , , ,

    Chance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only a limited information on the distribution is available, such as a sample from the distribution, or the moments of the distribution. We first … Read more

    Stochastic dual dynamic programming and its variants – a review

  • Christian Füllner
  • Steffen Rebennack
    • Categories Stochastic Programming Tags , , , , , , ,

    We provide a tutorial-type review on stochastic dual dynamic programming (SDDP), as one of the state-of-the-art solution methods for large-scale multistage stochastic programs. Since introduced about 30 years ago for solving large-scale multistage stochastic linear programming problems in energy planning, SDDP has been applied to practical problems from several fields and is enriched by various … Read more

    Multi-market Portfolio Optimization with Conditional Value at Risk

  • Luce Brotcorne
  • Martine Labbé
  • Stefano Nasini
    • Categories Applications - OR and Management Sciences, Cutting Plane Approaches, Integer Programming

    In this paper we propose an optimization framework for multi-markets portfolio management, where a central headquarter relies upon local affiliates for the market-wise selection of investment options. Being averse to risk, the headquarter endogenously selects the maximum expected loss (conditional value at risk) for the affiliates, who respond designing portfolios and selecting management fees. In … Read more

    TREGO: a Trust-Region Framework for Efficient Global Optimization

  • Youssef Diouane
  • V. Picheny
  • R. Le Riche
  • A. Scotto Di Perrotolo
    • Categories Global Optimization Tags , , ,

    Efficient Global Optimization (EGO) is the canonical form of Bayesian optimization that has been successfully applied to solve global optimization of expensive-to-evaluate black-box problems. However, EGO struggles to scale with dimension, and offers limited theoretical guarantees. In this work, a trust-region framework for EGO (TREGO) is proposed and analyzed. TREGO alternates between regular EGO steps … Read more

    Two-stage and one-group two-dimensional guillotine cutting problems with defects: a CP-based algorithm and ILP formulations

  • Mateus Martin
  • Reinaldo Morabito
  • Pedro Munari
    • Categories Applications - OR and Management Sciences Tags , , , , , ,

    We address two variants of the two-dimensional guillotine cutting problem that appear in different manufacturing settings that cut defective objects. Real-world applications include the production of flat glass in the glass industry and the cutting of wooden boards with knotholes in the furniture industry. These variants assume that there are several defects in the object, … Read more

    A top-down cutting approach for modeling the constrained two- and three-dimensional guillotine cutting problems

  • Mateus Martin
  • Reinaldo Morabito
  • Pedro Munari
    • Categories Applications - OR and Management Sciences Tags , ,

    In this paper, we address the Constrained Two-dimensional Guillotine Cutting Problem (C2GCP) and the Constrained Three-dimensional Guillotine Cutting Problem (C3GCP). These problems consist of cutting a rectangular two-/three-dimensional object with orthogonal guillotine cuts to produce ordered rectangular two-/three-dimensional items seeking the most valuable subset of items cut. They often appear in manufacturing settings that cut … Read more

    Three-dimensional guillotine cutting problems with constrained patterns: MILP formulations and a bottom-up algorithm

  • Mateus Martin
  • Reinaldo Morabito
  • Pedro Munari
  • José Fernando Oliveira
  • Elsa Silva
    • Categories Applications - OR and Management Sciences

    In this paper, we address the Constrained Three-dimensional Guillotine Cutting Problem (C3GCP), which consists of cutting a larger cuboid block (object) to produce a limited number of smaller cuboid pieces (items) using orthogonal guillotine cuts only. This way, all cuts must be parallel to the object’s walls and generate two cuboid sub-blocks, and there is … Read more

    Graph Coloring with Decision Diagrams

  • Willem-Jan van Hoeve
    • Categories Combinatorial Optimization, Integer Programming Tags , ,

    We introduce an iterative framework for solving graph coloring problems using decision diagrams. The decision diagram compactly represents all possible color classes, some of which may contain edge conflicts. In each iteration, we use a constrained minimum network flow model to compute a lower bound and identify conflicts. Infeasible color classes associated with these conflicts … Read more

    A Steepest Descent Method for Set Optimization Problems with Set-Valued Mappings of Finite Cardinality

  • Gemayqzel Bouza
  • Christiane Tammer
  • Ernest Quintana
    • Categories Multi-Criteria Optimization, Robust Optimization Tags , , ,

    In this paper, we study a first-order solution method for a particular class of set optimization problems where the solution concept is given by the set approach. We consider the case in which the set-valued objective mapping is identified by a finite number of continuously differentiable selections. The corresponding set optimization problem is then equivalent … Read more

    Vessel Deployment with Limited Information: Distributionally Robust Chance Constrained Models

  • Yue Zhao
  • Zhi Chen
  • Andrew Lim
  • Zhenzhen Zhang
    • Categories Robust Optimization, Transportation Tags , , , , ,

    This paper studies the fundamental vessel deployment problem in the liner shipping industry, which decides the numbers of mixed-type ships and their sailing frequencies on fixed routes to provide sufficient vessel capacity for fulfilling stochastic shipping demands with high probability. In reality, it is usually difficult (if not impossible) to acquire a precise joint distribution … Read more