Month: March 2022

A novel sequential optimality condition for smooth constrained optimization and algorithmic consequences

  • Renan W. Prado
  • Sandra A. Santos
  • Lucas E. A. Simões
    • Categories Constrained Nonlinear Optimization Tags , ,

    In the smooth constrained optimization setting, this work introduces the Domain Complementary Approximate Karush-Kuhn-Tucker (DCAKKT) condition, inspired by a sequential optimality condition recently devised for nonsmooth constrained optimization problems. It is shown that the augmented Lagrangian method can generate limit points satisfying DCAKKT, and it is proved that such a condition is not related to … Read more

    Two-Stage Robust Optimization with Decision Dependent Uncertainty

  • Bo Zeng
  • Wei Wang
    • Categories Robust Optimization Tags , ,

    The type of decision dependent uncertainties (DDUs) imposes a great challenge in decision making, while existing methodologies are not sufficient to support many real practices. In this paper, we present a systematic study to handle this challenge in two-stage robust optimization~(RO). Our main contributions include three sophisticated variants of column-and-constraint generation method to exactly compute … Read more

    Operation of an ambulance fleet under uncertainty

  • Victor Hugo Nascimento
  • Vincent Guigues
  • Anton J. Kleywegt
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , , ,

    We introduce two new optimization models for the dispatch of ambulances. These models are to our knowledge the first providing a full modelling of the operation of an ambulance fleet, taking into account all or almost all constraints of the problem. The first model, called the ambulance selection problem, is used when an emergency call … Read more

    Improving the global convergence of Inexact Restoration methods for constrained optimization problems

  • Roberto Andreani
  • Alberto Ramos
  • Leonardo D. Secchin
    • Categories Constrained Nonlinear Optimization Tags , , ,

    Inexact restoration (IR) methods are an important family of numerical methods for solving constrained optimization problems with applications to electronic structures and bilevel programming among others areas. In these methods, the minimization is divided in two phases: decreasing infeasibility (feasibility phase) and improving optimality (optimality phase). The feasibility phase does not require the generated points … Read more

    Modeling uncertainty processes for multi-stage optimization of strategic energy planning: An auto-regressive and Markov chain formulation

  • Esnil Guevara
  • Frédéric Babonneau
  • Tito Homem-de-Mello
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , ,

    This paper deals with the modeling of stochastic processes in long-term multistage energy planning problems when little information is available on the degree of uncertainty of such processes. Starting from simple estimates of variation intervals for uncertain parameters, such as energy demands and costs, we model the temporal correlation of these parameters through autoregressive (AR) … Read more

    Intraday Power Trading: Towards an Arms Race in Weather Forecasting?

  • Thomas Kuppelwieser
  • David Wozabal
    • Categories Finance and Economics, Stochastic Programming Tags , , , ,

    We propose the first weather-based algorithmic trading strategy on a continuous intraday power market. The strategy uses neither production assets nor power demand and generates profits purely based on superior information about aggregate output of weather-dependent renewable production. We use an optimized parametric policy based on state-of-the-art intraday updates of renewable production forecasts and evaluate … Read more

    Exact Solutions to a Carsharing Pricing and Relocation Problem under Uncertainty

  • Giovanni Pantuso
    • Categories Stochastic Programming, Transportation Tags , ,

    In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in … Read more

    Non-anticipative risk-averse analysis with effective scenarios applied to long-term hydrothermal scheduling

  • Karla Cristiane Arsie
  • Clóvis Caesar Gonzaga
  • Elizabeth Wegner Karas
  • Gislaine Aparecida Periçaro
    • Categories Nonlinear Optimization, Stochastic Programming Tags , , ,

    In this paper, we deal with long-term operation planning problems of hydrothermal power systems by considering scenario analysis and risk aversion. This is a stochastic sequential decision problem whose solution must be non-anticipative, in the sense that a decision at a stage cannot use a perfect knowledge of the future. We propose strategies to reduce … Read more

    An efficient solution methodology for the airport slot allocation problem with preprocessing and column generation

  • Paula Fermín Cueto
  • Sergio García
  • Miguel F. Anjos
    • Categories Airline Optimization, Scheduling, Transportation Tags , , , ,

    Airport coordination is a demand control mechanism that maximizes the use of existing infrastructure at congested airports. Aircraft operators submit a list of regular flights that they wish to operate over a five to seven-month period and a designated coordinator is responsible for allocating the available airport slots, which represent the permission to operate a … Read more

    Randomized Policy Optimization for Optimal Stopping

  • Xinyi Guan
  • Velibor Mišić
    • Categories Dynamic Programming, Finance and Economics, Unconstrained Optimization Tags , , , ,

    Optimal stopping is the problem of determining when to stop a stochastic system in order to maximize reward, which is of practical importance in domains such as finance, operations management and healthcare. Existing methods for high-dimensional optimal stopping that are popular in practice produce deterministic linear policies — policies that deterministically stop based on the … Read more