Applications – OR and Management Sciences

A Computational Study of Constraint Programming Approaches for Resource-Constrained Project Scheduling with Autonomous Learning Effects

  • Alessandro Hill
  • Jordan Ticktin
  • Thomas Vossen
    • Categories Combinatorial Optimization, Scheduling

    It is well-known that experience can lead to increased efficiency, yet this is largely unaccounted for in project scheduling. We consider project scheduling problems where the duration of activities can be reduced when scheduled after certain other activities that allow for learning relevant skills. Since per-period availabilities of renewable resources are limited and precedence requirements … Read more

    Improving Column-Generation for Vehicle Routing Problems via Random Coloring and Parallelization

  • Viswanath Nagarajan
  • Siqian Shen
  • Miao Yu
    • Categories Network Optimization, Parallel Algorithms, Transportation Tags , , , ,

    We consider a variant of the Vehicle Routing Problem (VRP) where each customer has a unit demand and the goal is to minimize the total cost of routing a fleet of capacitated vehicles from one or multiple depots to visit all customers. We propose two parallel algorithms to efficiently solve the column-generation based linear-programming relaxation … Read more

    Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting

  • Joaquim Dias Garcia
  • Tito Homem-de-Mello
  • Francisco D. Muñoz
  • Alexandre Street
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Optimization of Simulated Systems Tags , , , , ,

    Forecasting and decision-making are generally modeled as two sequential steps with no feedback, following an open-loop approach. In this paper, we present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized through a bilevel optimization problem. We present our methodology in a general format and prove … Read more

    An Almost Exact Multi-Machine Scheduling Solution for Homogeneous Processing

  • Stefano Nasini
  • Rabia Nessah
    • Categories (Mixed) Integer Nonlinear Programming, Combinatorial Optimization, Scheduling

    In the context of job scheduling in parallel machines, we present a class of asymptotically exact binary programs for the minimization of the $\tau$-norm of completion time variances. Building on overlooked properties of the min completion time variance in a single machine and on an equivalent bilevel formulation, our approach provides an asymptotic approximation (with … Read more

    A nonparametric algorithm for optimal stopping based on robust optimization

  • Bradley Sturt
    • Categories Finance and Economics, Robust Optimization, Stochastic Programming Tags , , ,

    Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for solving computationally- demanding stochastic optimal stopping problems with known probability distributions. The approach uses simulation to construct a robust optimization problem that approximates the stochastic optimal stopping problem to any … Read more

    A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

  • Daniel Kuhn
  • Napat Rujeerapaiboon
  • Kilian Schindler
  • Wolfram Wiesemann
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming

    Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bi-layer stochastic programming framework for the … 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

    Controllable Transmission Networks UnderDemand Uncertainty with Modular FACTS

  • Alireza Soroudi
    • Categories Constrained Nonlinear Optimization, Optimization Software and Modeling Systems, Scheduling Tags , ,

    The transmission system operators (TSOs) are responsible to provide secure and efficient access to the transmission system for all stakeholders. This task is gradually getting challenging due to the demand growth, demand uncertainty, rapid changes in generation mix, and market policies. Traditionally, the TSOs try to maximize the technical performance of the transmission network via … Read more

    A Comparative Study of Stability Representations for Solving Many-to-One Matching Problems with Utility-Weighted Objectives, Ties, and Incomplete Lists via Integer Optimization

  • Pitchaya Wiratchotisatian
  • Andrew C. Trapp
  • Hoda Atef Yekta
    • Categories Applications - OR and Management Sciences Tags , , , ,

    We consider integer optimization models for finding stable solutions to many-to-one, utility-weighted matching problems with incomplete preference lists and ties. While traditional algorithmic approaches for the stable many-to-one matching problem, such as the Deferred Acceptance algorithm, offer efficient performance for the strict problem setting, adaptation to alternative settings often requires careful customization. Optimization-based approaches are … Read more

    Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

  • Josh D'Aeth
  • Shubhechyya Ghosal
  • Fiona Grimm
  • David Haw
  • Esma Koca
  • Krystal Lau
  • Huikang Liu
  • Stefano Moret
  • Dheeya Rizmie
  • Peter Smith
  • Giovanni Forchini
  • Marisa Miraldo
  • Wolfram Wiesemann
    • Categories Applications - OR and Management Sciences, Dynamic Programming, Stochastic Programming Tags , , ,

    The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity so as to reduce the backlog of non-COVID patients whilst maintaining the ability to respond to any potential future … Read more