The two-echelon location-routing problem with time windows: Formulation, branch-and-price, and clustering

  • M. Saleh Farham
  • Cem Iyigun
  • Haldun Sural
    • Categories (Mixed) Integer Linear Programming, Transportation

    In this study, we consider the two-echelon location-routing problem with time windows (2E-LRPTW) to address the strategic and tactical decisions of the urban freight transportation. In the rst echelon, freights are delivered from city distribution centers (CDCs) to intermediate facilities, called satellites, in large batches. In the second echelon, goods are consolidated into smaller vehicles … Read more

    Some heuristic methods for the p-median problem with maximum distance constraints. Application to a bi-objective problem.

  • Adrián Esteban-Pérez
  • Jesús Sáez-Aguado
  • Luis A. San-José
    • Categories Facility Planning and Design, Meta Heuristics Tags , , , , ,

    In this work we study the p-median problem with maximum distance constraints (PMPDC) which is a variant of the classical p-median problem (PMP). First of all, we provide some different formulations for (PMPDC) because the heuristics procedures for the (PMPDC) with a formulation based on the approach that modifies the distance matrix that leads to … Read more

    Computing mixed strategies equilibria in presence of switching costs by the solution of nonconvex QP problems

  • Giampaolo Liuzzi
  • Marco Locatelli
  • Veronica Piccialli
  • Stefan Rass
    • Categories Game Theory, Global Optimization Applications, Quadratic Programming

    In this paper we address a game theory problem arising in the context of network security. In traditional game theory problems, given a defender and an attacker, one searches for mixed strategies which minimize a linear payoff functional. In the problem addressed in this paper an additional quadratic term is added to the minimization problem. … Read more

    Properties of the delayed weighted gradient method

  • Roberto Andreani
  • Marcos Raydan
    • Categories Unconstrained Optimization Tags , , , ,

    The delayed weighted gradient method, recently introduced in [13], is a low-cost gradient-type method that exhibits a surprisingly and perhaps unexpected fast convergence behavior that competes favorably with the well-known conjugate gradient method for the minimization of convex quadratic functions. In this work, we establish several orthogonality properties that add understanding to the practical behavior … Read more

    Near-optimal analysis of univariate moment bounds for polynomial optimization

  • Monique Laurent
  • Lucas Slot
    • Categories Constrained Nonlinear Optimization, Semi-definite Programming

    We consider a recent hierarchy of upper approximations proposed by Lasserre (arXiv:1907.097784, 2019) for the minimization of a polynomial f over a compact set K⊆ℝn. This hierarchy relies on using the push-forward measure of the Lebesgue measure on K by the polynomial f and involves univariate sums of squares of polynomials with growing degrees 2r. … Read more

    Exact and Heuristic Algorithms for the Carrier-Vehicle Traveling Salesman Problem

  • Gunes Erdogan
  • E. Alper Yildirim
    • Categories (Mixed) Integer Nonlinear Programming, Applications - OR and Management Sciences Tags , ,

    This paper presents new structural properties for the Carrier-Vehicle Traveling Salesman Problem. The authors provide a new mixed integer second order conic optimization formulation, with associated optimality cuts based on the structural properties, and an Iterated Local Search (ILS) algorithm. Computational experiments on instances from the literature demonstrate the superiority of the new formulation to … Read more

    Solving non-monotone equilibrium problems via a DIRECT-type approach

  • Stefano Lucidi
  • Mauro Passacantando
  • Francesco Rinaldi
    • Categories Complementarity and Variational Inequalities, Global Optimization Tags , , ,

    A global optimization approach for solving non-monotone equilibrium problems (EPs) is proposed. The class of (regularized) gap functions is used to reformulate any EP as a constrained global optimization program and some bounds on the Lipschitz constant of such functions are provided. The proposed global optimization approach is a combination of an improved version of … Read more

    Multistage Distributionally Robust Mixed-Integer Programming with Decision-Dependent Moment-Based Ambiguity Sets

  • Siqian Shen
  • Xian Yu
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization, Stochastic Programming Tags , , , ,

    We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical … Read more

    Distributionally Robust Bottleneck Combinatorial Problems: Uncertainty Quantification and Robust Decision Making

  • Shabbir Ahmed
  • Weijun Xie
  • Jie Zhang
    • Categories Stochastic Programming Tags , , , ,

    In a bottleneck combinatorial problem, the objective is to minimize the highest cost of elements of a subset selected from the combinatorial solution space. This paper studies data-driven distributionally robust bottleneck combinatorial problems (DRBCP) with stochastic costs, where the probability distribution of the cost vector is contained in a ball of distributions centered at the … Read more

    An Outer-approximation Guided Optimization Approach for Constrained Neural Network Inverse Problems

  • Myun-Seok Cheon
    • Categories Constrained Nonlinear Optimization, Nonsmooth Optimization

    This paper discusses an outer-approximation guided optimization method for constrained neural network inverse problems with rectified linear units. The constrained neural network inverse problems refer to an optimization problem to find the best set of input values of a given trained neural network in order to produce a predefined desired output in presence of constraints … Read more