stochastic programming

Algorithmic Approaches for Identifying the Trade-off between Pessimism and Optimism in a Stochastic Fixed Charge Facility Location Problem

  • Man Yiu Tsang
  • Karmel S. Shehadeh
    • Categories Facility Planning and Design, Robust Optimization, Stochastic Programming Tags , , ,

    We introduce new algorithms to identify the trade-off (TRO) between adopting a distributional belief and hedging against ambiguity when modeling uncertainty in a capacitated fixed charge facility location problem (CFLP). We first formulate a TRO model for the CFLP (TRO-CFLP), which determines the number of facilities to open by minimizing the fixed establishment cost and … Read more

    Data-Driven Multistage Scheduling Optimization for Refinery Production under Uncertainty: Systematic Framework, Modeling Approach, and Application Analysis

  • Biao Han
    • Categories Control Applications, Scheduling, Stochastic Programming Tags , , , , , , ,

    The widespread existence of various uncertainties makes the inherently complex refinery production scheduling problem even more challenging. To address this issue, this paper proposes a viable systematic data-driven multistage scheduling optimization framework and develops a corresponding structured modeling methodology. Under this paradigm, unit-level advanced control and plant-level intelligent scheduling are coordinated to jointly deal with … Read more

    An Adaptive Stochastic Dual Progressive Hedging Algorithm for Stochastic Programming

  • Di Zhang
  • Yihang Zhang
  • Suvrajeet Sen
    • Categories Stochastic Approaches, Stochastic Programming Tags , , ,

    The Progressive Hedging (PH) algorithm is one of the cornerstones in large-scale stochastic programming. However, its traditional development requires that all scenario subproblems are solved per iteration, and a probability distribution with finitely many outcomes. This paper introduces a stochastic dual PH algorithm (SDPH) to overcome these challenges. We introduce an adaptive sampling process and … Read more

    A Survey on the Applications of Stochastic Dual Dynamic Programming and its Variants

  • Bonn Kleiford Seranilla
  • Nils Löhndorf
    • Categories Dynamic Programming, Integer Programming, Stochastic Programming Tags , , ,

    Stochastic Dual Dynamic Programming (SDDP) is widely recognized as the predominant methodology for solving large-scale multistage stochastic linear programming (MSLP) problems. This paper aims to contribute to the extant literature by conducting a comprehensive survey of the literature on SDDP within the realm of practical applications. We systematically identify and analyze the various domains where … Read more

    Climate-Resilient Nodal Power System Expansion Planning for a Realistic California Test Case

  • Amelia Musselman
  • Tomas Valencia Zuluaga
  • Elizabeth Glista
  • Minda Monteagudo
  • J Michael Grappone
  • Matthew Signorotti
  • Jean-Paul Watson
    • Categories (Mixed) Integer Linear Programming, Energy, Stochastic Programming Tags , , , ,

    Climate change is increasingly impacting power system operations, not only through more frequent extreme weather events but also through shifts in routine weather patterns. Factors such as increased temperatures, droughts, changing wind patterns, and solar irradiance shifts can impact both power system production and transmission and electric load. The current power system was not designed … Read more

    Risk-Averse Antibiotics Time Machine Problem

  • Deniz Tuncer
  • Burak Kocuk
    • Categories (Mixed) Integer Linear Programming, Applications - Science and Engineering, Stochastic Programming Tags , , ,

    Antibiotic resistance, which is a serious healthcare issue, emerges due to uncontrolled and repeated antibiotic use that causes bacteria to mutate and develop resistance to antibiotics. The Antibiotics Time Machine Problem aims to come up with treatment plans that maximize the probability of reversing these mutations. Motivated by the severity of the problem, we develop … Read more

    Guaranteed bounds for optimal stopping problems using kernel-based non-asymptotic uniform confidence bands

  • Martin Glanzer
  • Sebastian Maier
  • Georg Ch. Pflug
    • Categories Dynamic Programming, Optimization in Data Science, Stochastic Programming Tags , , , ,

    In this paper, we introduce an approach for obtaining probabilistically guaranteed upper and lower bounds on the true optimal value of stopping problems. Bounds of existing simulation-and-regression approaches, such as those based on least squares Monte Carlo and information relaxation, are stochastic in nature and therefore do not come with a finite sample guarantee. Our … Read more

    Forecasting Outside the Box: Application-Driven Optimal Pointwise Forecasts for Stochastic Optimization

  • Tito Homem-de-Mello
    • Categories Data Science Algorithms, Stochastic Programming Tags ,

    The exponential growth in data availability in recent years has led to new formulations of data-driven optimization problems. One such formulation is that of stochastic optimization problems with contextual information, where the goal is to optimize the expected value of a certain function given some contextual information (also called features) that accompany the main data … Read more

    Single-Scenario Facet Preservation for Stochastic Mixed-Integer Programs

  • Aysenur Karagoz
    • Categories Integer Programming, Polyhedra, Stochastic Programming Tags , , ,

    We consider improving the polyhedral representation of the extensive form of a stochastic mixed-integer program (SMIP). Given a facet for a single-scenario version of an SMIP, our main result provides necessary and sufficient conditions under which this inequality remains facet-defining for the extensive form. We then present several implications, which show that common recourse structures … Read more

    Probing-Enhanced Stochastic Programming

  • Zhichao Ma
  • Youngdae Kim
  • Jeff Linderoth
  • James R. Luedtke
  • Logan Matthews
    • Categories Stochastic Programming Tags , , ,

    We consider a two-stage stochastic decision problem where the decision-maker has the opportunity to obtain information about the distribution of the random variables $\xi$ that appear in the problem through a set of discrete actions that we refer to as probing. Probing components of a random vector $\eta$ that is jointly-distributed with $\xi$ allows the … Read more