stochastic programming

Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty

  • Guangyu Li
  • Junyi Liu
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags ,

    Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the above sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Speci fically, for stochastic programming (SP) models with latently decision-dependent uncertainty, … 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

    Operations Planning Experiments for Power Systems with High Renewable Resources

  • Semih Atakan
  • Harsha Gangammanavar
  • Suvrajeet Sen
    • Categories Applications - Science and Engineering, Energy Tags , , , ,

    Driven by ambitious renewable portfolio standards, variable energy resources (such as wind and solar) are expected to impose unprecedented levels of uncertainty to power system operations. The current practice of planning operations with deterministic optimization tools may be ill-suited for a future where uncertainty is abundant. To overcome the reliability challenges associated with the large-scale … Read more

    Mixed Integer Programming models for planning maintenance at offshore wind farms under uncertainty

  • Albert H. Schrotenboer
  • Evrim Ursavas
  • Iris F. A. Vis
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming, Transportation Tags , , , , ,

    We introduce the Stochastic Maintenance Fleet Transportation Problem for Offshore wind farms (SMFTPO), in which a maintenance provider determines an optimal, medium-term planning for maintaining multiple wind farms while controlling for uncertainty in the maintenance tasks and weather conditions. Since the maintenance provider is typically not the owner of a wind farm, it needs to … Read more

    Adaptive Two-stage Stochastic Programming with an Application to Capacity Expansion Planning

  • Shabbir Ahmed
  • Nagi Gebraeel
  • Beste Basciftci
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , , , ,

    Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that are fully adapted to the uncertainty. Often, e.g. due to contractual constraints, such flexible and adaptive policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of … Read more

    Stochastic Lipschitz Dynamic Programming

  • Shabbir Ahmed
  • Filipe G. Cabral
  • Bernardo Freitas Paulo da Costa
    • Categories Dynamic Programming, Integer Programming, Stochastic Programming Tags , , ,

    We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. In a similar way to cutting plane methods, we construct nonlinear Lipschitz cuts to build lower approximations for the non-convex cost to go functions. An example of such a class of cuts are those derived using … Read more

    A Framework for Solving Chance-Constrained Linear Matrix Inequality Programs

  • Jianqiang Cheng
  • Miguel A. Lejeune
  • Roya Karimi
    • Categories Stochastic Programming Tags , , , ,

    We propose a novel partial sample average approximation (PSAA) framework to solve the two main types of chance-constrained linear matrix inequality (CCLMI) problems: CCLMI with random technology matrix, and CCLMI with random right-hand side. We propose a series of computationally tractable PSAA-based approximations for CCLMI problems, analyze their properties, and derive sufficient conditions ensuring convexity. … Read more

    Two-stage Stochastic Programming with Linearly Bi-parameterized Quadratic Recourse

  • Ying Cui
  • Junyi Liu
  • Jong-Shi Pang
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , ,

    This paper studies the class of two-stage stochastic programs (SP) with a linearly bi-parameterized recourse function defined by a convex quadratic program. A distinguishing feature of this new class of stochastic programs is that the objective function in the second stage is linearly parameterized by the first-stage decision variable, in addition to the standard linear … Read more

    Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming

  • Saeed Ghadimi
  • Raymond Perkins
  • Warren B. Powell
    • Categories Applications - OR and Management Sciences, Optimization of Simulated Systems, Stochastic Programming Tags , , , ,

    The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions (“dynamic programming”) or scenario trees (“stochastic programming”) to approximate the impact of a decision now on the future. By contrast, common industry practice is to use a deterministic approximation of the future which is easier … Read more