Month: October 2025

Multi-Modal Tsunami Evacuation Planning Considering Evacuees’ Non-Compliance Behavior: Istanbul Case Study

  • 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

    Political districting to maximize whole counties

  • Maral Shahmizad
  • Austin Buchanan
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Integer Programming Tags , , ,

    We consider a fundamental question in political districting: How many counties can be kept whole (i.e., not split across multiple districts), while satisfying basic criteria like contiguity and population balance? To answer this question, we propose integer programming techniques based on combinatorial Benders decomposition. The main problem decides which counties to keep whole, and the … Read more

    Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications

  • Carlos Gamboa
  • Alexandre Street
  • Davi Valladão
  • Bernardo K. Pagnoncelli
    • Categories Energy, Robust Optimization, Stochastic Programming Tags , , ,

    Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. This limitation hampers their use in online decision-making problems such as energy scheduling, where multiperiod optimization must be solved every few minutes. In this paper, we propose a novel Data-Driven Contextual Uncertainty Set, which gives … Read more

    Convexification of a Separable Function over a Polyhedral Ground Set

  • Santanu S. Dey
  • Burak Kocuk
    • Categories Cone Programming, Convex Optimization, Global Optimization Tags , , , , ,

    In this paper, we study the set \(\mathcal{S}^\kappa = \{ (x,y)\in\mathcal{G}\times\mathbb{R}^n : y_j = x_j^\kappa , j=1,\dots,n\}\), where \(\kappa > 1\) and the ground set \(\mathcal{G}\) is a nonempty polytope contained in \( [0,1]^n\). This nonconvex set is closely related to separable standard quadratic programming and appears as a substructure in potential-based network flow problems … Read more

    A Finite-Difference Trust-Region Method for Convexly Constrained Smooth Optimization

  • Dânâ Davar
  • Geovani Nunes Grapiglia
    • Categories Bound-constrained Optimization, Nonlinear Optimization Tags , , , ,

    We propose a derivative-free trust-region method based on finite-difference gradient approximations for smooth optimization problems with convex constraints. The proposed method does not require computing an approximate stationarity measure. For nonconvex problems, we establish a worst-case complexity bound of \(\mathcal{O}\!\left(n\left(\frac{L}{\sigma}\epsilon\right)^{-2}\right)\) function evaluations for the method to reach an \(\left(\frac{L}{\sigma}\epsilon\right)\)-approximate stationary point, where \(n\) is the … Read more

    An Inexact General Descent Method with Applications in Differential Equation-Constrained Optimization

  • Humberto Gimenes Macedo
  • Luís Felipe Bueno
    • Categories Nonlinear Optimization, Systems governed by Differential Equations Optimization Tags , , ,

    In many applications, gradient evaluations are inherently approximate, motivating the development of optimization methods that remain reliable under inexact first-order information. A common strategy in this context is adaptive evaluation, whereby coarse gradients are used in early iterations and refined near a minimizer. This is particularly relevant in differential equation–constrained optimization (DECO), where discrete adjoint … 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

    A Simple First-Order Algorithm for Full-Rank Equality Constrained Optimization

  • Serge Gratton
  • Philippe L. Toint
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Nonlinear Systems and Least-Squares Tags , , , ,

    A very simple first-order algorithm is proposed for solving nonlinear optimization problems with deterministic nonlinear equality constraints. This algorithm adaptively selects steps in the plane tangent to the constraints or steps that reduce infeasibility, without using a merit function or a filter. The tangent steps are based on the AdaGrad method for unconstrained minimization. The … Read more

    The Branch-and-Bound Tree Closure

  • Marius Roland
  • Nagisa Sugishita
  • Alexandre Forel
  • Youssouf Emine
  • Ricardo Fukasawa
  • Thibaut Vidal
    • Categories (Mixed) Integer Linear Programming, 0-1 Programming, Cutting Plane Approaches Tags , ,

    This paper investigates the a-posteriori analysis of Branch-and-Bound (BB) trees to extract structural information about the feasible region of mixed-binary linear programs. We introduce three novel outer approximations of the feasible region, systematically constructed from a BB tree. These are: a tight formulation based on disjunctive programming, a branching-based formulation derived from the tree’s branching … Read more