Stochastic Programming

A Data-Driven Approach for a Class of Stochastic Dynamic Optimization Problems

  • Tito Homem-de-Mello
  • Thuener Silva
  • Davi Valladão
    • Categories Stochastic Programming Tags , , ,

    Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and … Read more

    Stochastic Optimization Models of Insurance Mathematics

  • Yuri M. Ermoliev
  • Bogdan V. Norkin
  • Vladimir I. Norkin
    • Categories Finance and Economics, Multi-Criteria Optimization, Stochastic Programming Tags , , , , , , , , ,

    The paper overviews stochastic optimization models of insurance mathematics and methods for their solution from the point of view of stochastic programming and stochastic optimal control methodology, with vector optimality criteria. The evolution of an insurance company’s capital is considered in discrete time. The main random variables, which influence this evolution, are levels of payments, … Read more

    Stochastic DC Optimal Power Flow With Reserve Saturation

  • Rohit Kannan
  • James R. Luedtke
  • Line Roald
    • Categories Applications - Science and Engineering, Stochastic Programming Tags , , , ,

    We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits … Read more

    Admissibility of solution estimators for stochastic optimization

  • Amitabh Basu
  • Tu Nguyen
  • Ao Sun
    • Categories Stochastic Programming Tags , , ,

    We look at stochastic optimization problems through the lens of statistical decision theory. In particular, we address admissibility, in the statistical decision theory sense, of the natural sample average estimator for a stochastic optimization problem (which is also known as the empirical risk minimization (ERM) rule in learning literature). It is well known that for … Read more

    Joint chance-constrained programs and the intersection of mixing sets through a submodularity lens

  • Fatma Kılınç-Karzan
  • Simge Küçükyavuz
  • Dabeen Lee
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , ,

    A particularly important substructure in modeling joint linear chance-constrained programs with random right-hand sides and finite sample space is the intersection of mixing sets with common binary variables (and possibly a knapsack constraint). In this paper, we first revisit basic mixing sets by establishing a strong and previously unrecognized connection to submodularity. In particular, we … Read more

    Improving sample average approximation using distributional robustness

  • Edward Anderson
  • Andrew B. Philpott
    • Categories Stochastic Programming Tags , ,

    We consider stochastic optimization problems in which we aim to minimize the expected value of an objective function with respect to an unknown distribution of random parameters. We analyse the out-of-sample performance of solutions obtained by solving a distributionally robust version of the sample average approximation problem for unconstrained quadratic problems, and derive conditions under … Read more

    Optimal Crashing of an Activity Network with Disruptions

  • David P. Morton
  • Haoxiang Yang
    • Categories Stochastic Programming Tags , , ,

    In this paper, we consider an optimization problem involving crashing an activity network under a single disruption. A disruption is an event whose magnitude and timing are random. When a disruption occurs the duration of an activity, which has not yet started, can change. We formulate a two-stage stochastic mixed integer program, in which the … Read more

    Stochastic Dynamic Linear Programming: A Sequential Sampling Algorithm for Multistage Stochastic Linear Programming

  • Harsha Gangammanavar
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , , ,

    Multistage stochastic programming deals with operational and planning problems that involve a sequence of decisions over time while responding to realizations that are uncertain. Algorithms designed to address multistage stochastic linear programming (MSLP) problems often rely upon scenario trees to represent the underlying stochastic process. When this process exhibits stagewise independence, sampling-based techniques, particularly the … Read more

    Stochastic generalized gradient methods for training nonconvex nonsmooth neural networks

  • Vladimir I. Norkin
    • Categories Nonlinear Optimization, Nonsmooth Optimization, Stochastic Programming Tags , , , , ,

    The paper observes a similarity between the stochastic optimal control of discrete dynamical systems and the learning multilayer neural networks. It focuses on contemporary deep networks with nonconvex nonsmooth loss and activation functions. The machine learning problems are treated as nonconvex nonsmooth stochastic optimization problems. As a model of nonsmooth nonconvex dependences, the so-called generalized … Read more

    Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach

  • Ruiwei Jiang
  • Minseok Ryu
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming

    We study the nurse staffing problem under random nurse demand and absenteeism. While the demand uncertainty is exogenous (stemming from the random patient census), the absenteeism uncertainty is endogenous, i.e., the number of nurses who show up for work partially depends on the nurse staffing level. For the quality of care, many hospitals have developed … Read more