endogenous uncertainty

A decomposition strategy for decision problems with endogenous uncertainty using mixed-integer programming

  • Olli Herrala
  • Fabricio Oliveira
  • Tommi Ekholm
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , ,

    Decision problems under endogenous uncertainty are still challenging to solve, despite the advances in solution methods and increasing computational power. A novel framework called Decision Programming solves such decision problems using off-the-shelf solvers by using influence diagrams to represent decision problems with decision-dependent probabilities, and then converting the influence diagram representation of the problem to … Read more

    Capacity planning with uncertain endogenous technology learning

  • Tushar Rathi
  • Qi Zhang
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , , ,

    Optimal capacity expansion requires complex decision-making, often influenced by technology learning, which represents the reduction in expansion cost due to factors such as cumulative installed capacity. However, having perfect foresight over the technology cost reduction is highly unlikely. In this work, we develop a multistage stochastic programming framework to model capacity planning problems with endogenous … Read more

    A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

  • Wei Feng
  • Qi Zhang
    • Categories Robust Optimization Tags , ,

    This work proposes a framework for multistage adjustable robust optimization that unifies the treatment of three different types of endogenous uncertainty, where decisions, respectively, (i) alter the uncertainty set, (ii) affect the materialization of uncertain parameters, and (iii) determine the time when the true values of uncertain parameters are observed. We provide a systematic analysis … Read more

    ROC++: Robust Optimization in C++

  • Phebe Vayanos
  • Qing Jin
  • George Elissaios
    • Categories Optimization Software and Modeling Systems, Robust Optimization Tags , , , , , ,

    Over the last two decades, robust optimization has emerged as a popular means to address decision-making problems affected by uncertainty. This includes single- and multi-stage problems involving real-valued and/or binary decisions, and affected by exogenous (decision-independent) and/or endogenous (decision-dependent) uncertain parameters. Robust optimization techniques rely on duality theory potentially augmented with approximations to transform a … Read more

    Multistage Distributionally Robust Mixed-Integer Programming with Decision-Dependent Moment-Based Ambiguity Sets

  • Siqian Shen
  • Xian Yu
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization, Stochastic Programming Tags , , , ,

    We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical … Read more

    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

    Robust Optimization with Decision-Dependent Information Discovery

  • Phebe Vayanos
  • Angelos Georghiou
  • Han Yu
    • Categories Robust Optimization Tags , , ,

    Robust optimization (RO) is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, such as R&D project selection, production planning, or preference elicitation for product or policy recommendations, the time of information discovery is decision-dependent and the uncertain parameters only become observable after an often costly … Read more

    Robust Optimization for Decision-making under Endogenous Uncertainty

  • Chrysanthos E. Gounaris
  • Nikolaos Lappas
    • Categories Robust Optimization Tags , ,

    This paper contemplates the use of robust optimization as a framework for addressing problems that involve endogenous uncertainty, i.e., uncertainty that is affected by the decision maker’s strategy. To that end, we extend generic polyhedral uncertainty sets typically considered in robust optimization into sets that depend on the actual decisions. We present the derivation of … Read more