benders decomposition

Normalization of ReLU Dual for Cut Generation in Stochastic Mixed-Integer Programs

  • Akul Bansal
  • Simge Küçükyavuz
    • Categories Stochastic Programming Tags , ,

    We study the Rectified Linear Unit (ReLU) dual, an existing dual formulation for stochastic programs that reformulates non-anticipativity constraints using ReLU functions to generate tight, non-convex, and mixed-integer representable cuts. While this dual reformulation guarantees convergence with mixed-integer state variables, it admits multiple optimal solutions that can yield weak cuts. To address this issue, we … Read more

    Combinatorial Benders Decomposition and Column Generation for Optimal Box Selection

  • Christoph Buchheim
  • Alice Kirchheim
  • Pia Schreynemackers
    • Categories Combinatorial Optimization, Cutting Plane Approaches, Transportation Tags , ,

    We consider a two-stage optimization problem with sparsity constraints, motivated by a common challenge in packaging logistics: minimizing the volume of transported air by optimizing the size and number of available packaging boxes, given the demand for order items. In the first stage, we select the optimal dimensions of the boxes, while in the second … Read more

    A Framework for Handling and Exploiting Symmetry in Benders’ Decomposition

  • Christopher Hojny
  • Cedric Roy
    • Categories (Mixed) Integer Linear Programming, Integer Programming, Scheduling Tags ,

    Benders’ decomposition (BD) is a framework for solving optimization problems by removing some variables and modeling their contribution to the original problem via so-called Benders cuts. While many advanced optimization techniques can be applied in a BD framework, one central technique has not been applied systematically in BD: symmetry handling. The main reason for this … Read more

    Stronger cuts for Benders’ decomposition for stochastic Unit Commitment Problems based on interval variables

  • Azema
  • Vincent Leclère
  • Wim van Ackooij
    • Categories (Mixed) Integer Linear Programming, Energy, Stochastic Programming Tags , ,

    The Stochastic Unit Commitment (SUC) problem models the scheduling of power generation units under uncertainty, typically using a two-stage stochastic program with integer first-stage and continuous second-stage variables. We propose a new Benders decomposition approach that leverages an extended formulation based on interval variables, enabling decomposition by both unit and time interval under mild technical … Read more

    Locating Temporary Hospitals and Transporting the Injured Equitably in Disasters

  • Shiva Moslemi
  • Secil Savasaneril
  • Mustafa Tural
    • Categories Applications - OR and Management Sciences, Transportation Tags , , , , , , , ,

    In the aftermath of an earthquake, one of the most critical needs is medical care for the injured. A large number of individuals require immediate attention, often overwhelming the available healthcare resources. The sudden surge in demand, coupled with limited resources, route congestion and infrastructure damage, makes immediate medical care provision a significant challenge. This … Read more

    Behaviorally Informed Planning for Retrofitting, Sheltering, and Mixed-Mode Evacuation in Urban Tsunami Preparedness: Toward a Tsunami-Resilient Istanbul

  • Shiva Moslemi
  • Vedat Bayram
  • Baris Yildiz
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Transportation Tags , , , , ,

    Tsunamis, primarily triggered by earthquakes, pose critical threats to coastal populations due to their rapid onset and limited evacuation time. Two main protective actions exist: sheltering in place, which requires substantial retrofitting investments, and evacuation, which is often hindered by congestion, mixed travel modes, and tight inundation times. Given pedestrians’ slower movement and restricted evacuation … Read more

    Effective Solution Algorithms for Bulk-Robust Optimization Problems

  • Christoph Buchheim
  • Enrico Malaguti
  • Michele Monaci
  • Paolo Paronuzzi
  • Pia Schreynemackers
    • Categories 0-1 Programming, Network Optimization, Robust Optimization Tags , , , ,

    Bulk-robust optimization is a recent paradigm for addressing problems in which the structure of a system is affected by uncertainty. It considers the case in which a finite and discrete set of possible failure scenarios is known in advance, and the decision maker aims to activate a subset of available resources of minimum cost so … Read more

    The Surprising Performance of Random Partial Benders Decomposition

  • Jean Pauphilet
  • Yupeng Wu
    • Categories (Mixed) Integer Linear Programming, Integer Programming, Stochastic Programming Tags ,

    Benders decomposition is a technique to solve large-scale mixed-integer optimization problems by decomposing them into a pure-integer master problem and a continuous separation subproblem. To accelerate convergence, we propose Random Partial Benders Decomposition (RPBD), a decomposition method that randomly retains a subset of the continuous variables within the master problem. Unlike existing problem-specific approaches, RPBD … Read more

    Approximating value functions via corner Benders’ cuts

  • Matheus Jun Ota
  • Ricardo Fukasawa
  • Aleksandr M. Kazachkov
    • Categories Branch and Cut Algorithms, Integer Programming, Stochastic Programming Tags , , , , ,

    We introduce a novel technique to generate Benders’ cuts from a conic relaxation (“corner”) derived from a basis of a higher-dimensional polyhedron that we aim to outer approximate in a lower-dimensional space. To generate facet-defining inequalities for the epigraph associated to this corner, we develop a computationally-efficient algorithm based on a compact reverse polar formulation … Read more

    Extracting Alternative Solutions from Benders Decomposition

  • Matthew Viens
  • William E. Hart
  • Michael C. Ferris
    • Categories (Mixed) Integer Linear Programming, Linear Programming, Stochastic Programming Tags , , , , ,

    We show how to extract alternative solutions for optimization problems solved by Benders Decom- position. In practice, alternative solutions provide useful insights for complex applications; some solvers do support generation of alternative solutions but none appear to support such generation when using Benders Decomposition. We propose a new post-processing method that extracts multiple optimal and … Read more