Bernardo K. Pagnoncelli

Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications

  • Carlos Gamboa
  • Alexandre Street
  • Davi Valladão
  • Bernardo K. Pagnoncelli
    • Categories Energy, Robust Optimization, Stochastic Programming Tags , , ,

    Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. This limitation hampers their use in online decision-making problems such as energy scheduling, where multiperiod optimization must be solved every few minutes. In this paper, we propose a novel Data-Driven Contextual Uncertainty Set, which gives … Read more

    MDP modeling for multi-stage stochastic programs

  • David P. Morton
  • Oscar Dowson
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , , ,

    We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes structured MDPs with continuous state and action spaces. We extend policy graphs to include decision-dependent uncertainty for one-step transition probabilities as well as a limited form of statistical learning. We focus on the expressiveness of … Read more

    Pessimistic bilevel optimization approach for decision-focused learning

  • Diego Jiménez
  • Bernardo K. Pagnoncelli
  • Hande Yaman
    • Categories 0-1 Programming, Stochastic Programming Tags , ,

    The recent interest in contextual optimization problems, where randomness is associated with side information, has led to two primary strategies for formulation and solution. The first, estimate-then-optimize, separates the estimation of the problem’s parameters from the optimization process. The second, decision-focused optimization, integrates the optimization problem’s structure directly into the prediction procedure. In this work, … Read more

    Contextual Stochastic Programs with Expected-Value Constraints

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , ,

    Expected-value-constrained programming (ECP) formulations are a broad class of stochastic programming problems including integrated chance constraints, risk models, and stochastic dominance formulations. Given the wide availability of data, it is common in applications to have independent contextual information associated with the target or dependent random variables of the problem. We show how to incorporate such … Read more

    Data-Driven Approximation of Contextual Chance-Constrained Stochastic Programs

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , ,

    Uncertainty in classical stochastic programming models is often described solely by independent random parameters, ignoring their dependence on multidimensional features. We describe a novel contextual chance-constrained programming formulation that incorporates features, and argue that solutions that do not take them into account may not be implementable. Our formulation cannot be solved exactly in most cases, … Read more

    What is the optimal cutoff surface for ore bodies with more than one mineral?

  • Lewis Ntaimo
  • Bernardo K. Pagnoncelli
  • A. Piazza
    • Categories Applications - OR and Management Sciences, Control Applications, Dynamic Programming

    In mine planning problems, cutoff grade optimization defines a threshold at every time period such that material above this value is processed, and the rest is considered waste. In orebodies with multiple minerals, which occur in practice, the natural extension is to consider a cutoff surface. We show that in two dimensions the optimal solution … Read more

    Contextual Chance-Constrained Programming

  • Hamed Rahimian
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming Tags , , , ,

    Uncertainty in classical stochastic programming models is often described solely by independent random parameters, ignoring their dependence on multidimensional features. We describe a novel contextual chance-constrained programming formulation that incorporates features, and argue that solutions that do not take them into account may not be implementable. Our formulation cannot be solved exactly in most cases, … Read more

    Risk-Averse Multistage Stochastic Programs with Expected Conditional Risk Measures

  • Maryam Khatami
  • Lewis Ntaimo
  • Bernardo K. Pagnoncelli
  • Thuener Silva
    • Categories Stochastic Programming Tags , ,

    We study decomposition algorithms for risk-averse multistage stochastic programs with expected conditional risk measures (ECRMs). ECRMs are attractive because they are time-consistent, which means that a plan made today will not be changed in the future if the problem is re-solved given a realization of the random variables. We show that solving risk-averse problems based … Read more

    Multistage stochastic programs with the entropic risk measure

  • Oscar Dowson
  • David P. Morton
  • Bernardo K. Pagnoncelli
    • Categories Stochastic Programming

    Over the last two decades, coherent risk measures have been well studied as a principled, axiomatic way to measure the risk of a random variable. Because of this axiomatic approach, coherent risk measures have a number of attractive features for computation, and they have been integrated into a variety of stochastic programming algorithms, including stochastic … Read more

    The risk-averse ultimate pit problem

  • Gianpiero Canessa
  • Bernardo K. Pagnoncelli
  • Eduardo Moreno
    • Categories 0-1 Programming, Stochastic Programming

    In this work, we consider a risk-averse ultimate pit problem where the grade of the mineral is uncertain. We propose a two-stage formulation of the problem and discuss which properties are desirable for a risk measure in this context. We show that the only risk measure that satisfies these properties is the entropic. We propose … Read more