benders decomposition

Hub Network Design Problem with Capacity, Congestion and Stochastic Demand Considerations

  • Vedat Bayram
  • Barış Yıldız
  • M. Saleh Farham
    • Categories Second-Order Cone Programming, Stochastic Programming, Transportation Tags , , , , , , ,

    We introduce the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decision and congestion cost effect into the problem and allowing dynamic routing for origin-destination pairs. Connecting strategic and operational level decisions, … Read more

    Adjustable robust optimization with discrete uncertainty

  • Henri Lefebvre
  • Enrico Malaguti
  • Michele Monaci
    • Categories Robust Optimization Tags , , ,

    In this paper, we study Adjustable Robust Optimization (ARO) problems with discrete uncertainty. Under a very general modeling framework, we show that such two-stage robust problems can be exactly reformulated as ARO problems with objective uncertainty only. This reformulation is valid with and without the fixed recourse assumption and is not limited to continuous wait-and-see … Read more

    Determining locations and layouts for parcel lockers to support supply chain viability at the last mile

  • Michael Kahr
    • Categories (Mixed) Integer Linear Programming, Facility Planning and Design, Transportation Tags , , , ,

    The pandemic caused by the corona virus SARS-CoV-2 raised many new challenges for humanity. For instance, governments imposed regulations such as lockdowns, resulting in supply chain shocks at different tiers. Additionally, delivery services reached their capacity limits because the demand for mail orders soared temporarily during the lockdowns. We argue that one option to support … Read more

    The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach

  • Pirmin Fontaine
  • Stefan Minner
  • Santiago Nieto-Isaza
    • Categories Applications - OR and Management Sciences, Supply Chain Management, Transportation Tags , , ,

    Crowd-shipping is an emergent solution to avoid the negative effects caused by the growing demand for last-mile delivery services. Previous research has studied crowd-shipping typically at an operational planning level. However, the study of support infrastructure within a city logistics framework has been neglected, especially from a strategic perspective. We investigate a crowd-sourced last-mile parcel … Read more

    The Stochastic Pseudo-Star Degree Centrality Problem

  • Mustafa Can Camur
  • Thomas C. Sharkey
  • Chrysafis Vogiatzis
    • Categories Combinatorial Optimization, Integer Programming Tags , , ,

    We introduce the stochastic pseudo-star degree centrality problem, which focuses on a novel probabilistic group-based centrality metric. The goal is to identify a feasible induced pseudo-star, which is defined as a collection of nodes forming a star network with a certain probability, such that it maximizes the sum of the individual probabilities of unique assignments … Read more

    A tailored Benders decomposition approach for last-mile delivery with autonomous robots

  • Laurent Alfandari
  • Ivana Ljubić
  • Marcos de Melo da Silva
    • Categories Branch and Cut Algorithms, Network Optimization, Transportation Tags , , ,

    This work addresses an operational problem of a logistics service provider that consists of finding an optimal route for a vehicle carrying customer parcels from a central depot to selected facilities, from where autonomous devices like robots are launched to perform last-mile deliveries. The objective is to minimize a tardiness indicator based on the customer … Read more

    On the Structure of Decision Diagram-Representable Mixed Integer Programs with Application to Unit Commitment

  • Hosseinali Salemi
  • Danial Davarnia
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Energy Tags , , ,

    Over the past decade, decision diagrams (DDs) have been used to model and solve integer programming and combinatorial optimization problems. Despite successful performance of DDs in solving various discrete optimization problems, their extension to model mixed integer programs (MIPs) such as those appearing in energy applications has been lacking. More broadly, the question on which … Read more

    Strong Optimal Classification Trees

  • Sina Aghaei
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Statistics Tags , , ,

    Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this paper, we consider the problem of learning optimal binary classification trees with univariate splits. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality … Read more

    Efficient presolving methods for the influence maximization problem in social networks

  • Sheng-Jie Chen
  • Wei-Kun Chen
  • Yu-Hong Dai
  • Jian-Hua Yuan
  • Hou-Shan Zhang
    • Categories (Mixed) Integer Linear Programming Tags , , , , ,

    We consider the influence maximization problem (IMP) which asks for identifying a limited number of key individuals to spread influence in a social network such that the expected number of influenced individuals is maximized. The stochastic maximal covering location problem (SMCLP) formulation is a mixed integer programming formulation that effectively approximates the IMP by the … Read more

    A converging Benders’ decomposition algorithm for two-stage mixed-integer recourse models

  • Ward Romeijnders
  • Niels van der Laan
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , ,

    We propose a new solution method for two-stage mixed-integer recourse models. In contrast to existing approaches, we can handle general mixed-integer variables in both stages, and thus, e.g., do not require that the first-stage variables are binary. Our solution method is a Benders’ decomposition, in which we iteratively construct tighter approximations of the expected second-stage … Read more