Applications – OR and Management Sciences

New Dynamic Discretization Discovery Strategies for Continuous-Time Service Network Design

  • Shengnan Shu
  • Zhou Xu
  • Roberto Baldacci
    • Categories Applications - OR and Management Sciences, Integer Programming, Network Optimization Tags , , ,

    Service Network Design Problems (SNDPs) are prevalent in the freight industry. While the classic SNDP is defined on a discretized planning horizon with integral time units, the Continuous-Time SNDP (CTSNDP) uses a continuous-time horizon to avoid discretization errors. Existing CTSNDP algorithms primarily rely on the Dynamic Discretization Discovery (DDD) framework, which iteratively refines discretization and … Read more

    The robust pickup and delivery problem with time windows

  • Alex Abreu
  • Maria Battarra
  • Luciano Costa
  • Pedro Munari
    • Categories Applications - OR and Management Sciences, Transportation Tags , , , ,

    This study addresses the robust pickup and delivery problem with time windows (RPDPTW), in which uncertainty in demands and travel times is modelled using robust optimisation. The RPDPTW involves determining the least-cost routes to serve transportation requests from origins to destinations, while respecting vehicle capacity and time window constraints under all anticipated realisations of uncertain … Read more

    Mixed Integer Linear Programming Formulations for Robust Surgery Scheduling

  • Ankit Bansal
    • Categories (Mixed) Integer Linear Programming, Robust Optimization, Scheduling

    We introduce Mixed Integer Linear Programming (MILP) formulations for the two-stage robust surgery scheduling problem (2SRSSP). We derive these formulations by modeling the second-stage problem as a longest path problem on a layered acyclic graph and subsequently converting it into a linear program. This linear program is then dualized and integrated with the first-stage, resulting … Read more

    Multiple Kernel Learning-Aided Column-and-Constraint Generation Method

  • Biao Han
    • Categories Applications - OR and Management Sciences, Optimization in Data Science, Robust Optimization Tags , , , ,

    Two-stage robust optimization (two-stage RO), due to its ability to balance robustness and flexibility, has been widely used in various fields for decision-making under uncertainty. This paper proposes a multiple kernel learning (MKL)-aided column-and-constraint generation (CCG) method to address this issue in the context of data-driven decision optimization, and releases a corresponding registered Julia package, … Read more

    Reduction from the partition problem: Dynamic lot sizing problem with polynomial complexity

  • Chee-Khian Sim
    • Categories Dynamic Programming, Production and Logistics, Supply Chain Management

    In this note, we polynomially reduce an instance of the partition problem to a dynamic lot sizing problem, and show that solving the latter problem solves the former problem. By solving the dynamic program formulation of the dynamic lot sizing problem, we show that the instance of the partition problem can be solved with pseudo-polynomial … Read more

    Gradient-Driven Solution Based on Indifference Analysis (GIA) for Scenario Modelling Optimization Problem

  • Vincent Yuansang Zha
    • Categories Applications - OR and Management Sciences, Finance and Economics, Nonlinear Optimization Tags , , ,

    This paper introduces an optimization technique for scenario modeling in uncertain business situations, termed the Gradient-Driven Solution Based on Indifference Analysis (GIA). GIA evolves the conventional methods of scenario planning by applying a reverse-strategy approach, where future financial goals are specified, and the path to attain these targets are engineered backward. It adopts economic concepts … Read more

    Neural Embedded Mixed-Integer Optimization for Location-Routing Problems

  • Waquar Kaleem
  • Anirudh Subramanyam
    • Categories (Mixed) Integer Linear Programming, Facility Planning and Design, Production and Logistics Tags , , ,

    We present a novel framework that combines machine learning with mixed-integer optimization to solve the Capacitated Location-Routing Problem (CLRP). The CLRP is a classical yet NP-hard problem that integrates strategic facility location with operational vehicle routing decisions, aiming to simultaneously minimize both fixed and variable costs. The proposed method first trains a permutationally invariant neural … Read more

    Enhancing Top Efficiency by Minimizing Second-Best Scores: A Novel Perspective on Super Efficiency Models in DEA

  • Tomonari Kitahara
  • Takashi Tsuchiya
    • Categories Applications - OR and Management Sciences, Linear, Cone and Semidefinite Programming, Optimization in Data Science Tags , ,

    In this paper, we reveal a new characterization of the super-efficiency model for Data Envelopment Analysis (DEA). In DEA, the efficiency of each decision making unit (DMU) is measured by the ratio the weighted sum of outputs divided by the weighted sum of inputs.In order to measure efficiency of a DMU, ${\rm DMU}_j$, say, in CCR … Read more

    An Efficient Algorithm to the Integrated Shift and Task Scheduling Problem

  • G S R Murthy
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Scheduling Tags , , , , ,

    Abstract   This paper deals with operational models for integrated shift and task scheduling problem. Staff scheduling problem is a special case of this with staff requirements as given input to the problem. Both problems become hard to solve when the problems are considered with flexible shifts. Current literature on these problems leaves good scope … Read more

    A Branch-and-Price-and-Cut Algorithm for Discrete Network Design Problems Under Traffic Equilibrium

  • David Rey
  • Michael Levin
    • Categories (Mixed) Integer Nonlinear Programming, Bilevel Optimization, Transportation Tags , , , ,

    This study addresses discrete network design problems under traffic equilibrium conditions or DNDPs. Given a network and a budget, DNDPs aim to model all-or-nothing decisions such as link addition to minimize network congestion effects. Congestion is measured using traffic equilibrium theory where link travel times are modeled as convex flow-dependent functions and where users make … Read more