Stochastic Programming

Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance

  • Zhuojun Xie
  • Adam Abdin
  • Yiping Fang
    • Categories Optimization in Data Science, Stochastic Programming Tags , , , ,

    Recent research increasingly integrates machine learning (ML) into predictive maintenance (PdM) to reduce operational and maintenance costs in data-rich operational settings. However, uncertainty due to model misspecification continues to limit widespread industrial adoption. This paper investigates a PdM framework in which sensor-driven prognostics inform decision-making under economic trade-offs within a finite decision space. We investigate … Read more

    The L-Shaped Method for Stochastic Programs with Decision-Dependent Uncertainty

  • Giovanni Pantuso
  • Mike Hewitt
    • Categories Stochastic Programming

    In this paper we extend the well-known L-Shaped method to solve two-stage stochastic programming problems with decision-dependent uncertainty. The method is based on a novel, unifying, formulation and on distribution-specific optimality and feasibility cuts for both linear and integer stochastic programs. Extensive tests on three production planning problems illustrate that the method is extremely effective … Read more

    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

    ASMOP: Additional sampling stochastic trust region method for multi-objective problems

  • Nataša Krklec Jerinkić
  • Luka Rutešić
  • Ilaria Trombini
    • Categories Multi-Criteria Optimization, Optimization of Systems modeled by PDEs, Stochastic Programming Tags , , , , ,

    We consider unconstrained multi-criteria optimization problems with finite sum objective functions. The proposed algorithm belongs to a non-monotone trust region framework where additional sampling approach is used to govern the sample size and the acceptance of a candidate point. Depending on the problem, the method can yield a mini-batch or an increasing sample size behavior. … Read more

    Statistical Inference for Distributed Contextual Multi-armed Bandit

  • Wuwenqing Yan
  • Yongchao Liu
    • Categories Stochastic Programming Tags , , , ,

    In this paper, we study the online statistical inference of distributed contextual multi-armed bandit problems, where the agents collaboratively learn an optimal policy by exchanging their local estimates of the global parameters with neighbors over a communication network. We propose a distributed online decision making algorithm, which balances the exploration and exploitation dilemma via the … 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

    On Multi-Disjunctive Valid Inequalities for Chance-Constrained Stochastic Problems with Finite Support

  • Diego Cattaruzza
  • Martine Labbé
  • Matteo Petris
  • Marius Roland
  • Martin Schmidt
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , , , ,

    We consider mixed-integer linear chance-constrained problems for which the random vector that parameterizes the feasible region has finite support. Our key objective is to improve branch-and-bound or -cut approaches by introducing new types of valid inequalities that improve the dual bound and, by this, the overall performance of such methods. We introduce so-called multi-disjunctive valid … Read more

    Multi-cut stochastic approximation methods for solving stochastic convex composite optimization

  • Jiaming Liang
  • Honghao Zhang
  • Renato D.C. Monteiro
    • Categories Convex Optimization, Stochastic Programming Tags , , ,

    This paper considers the stochastic convex composite optimization problem and presents multi-cut stochastic approximation (SA) methods for solving it, whose models in expectation overestimate its objective function. The multi-cut model obtained by taking the maximum of a finite number of linearizations of the stochastic objective function provides a biased estimate of the objective function, with … Read more

    Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization

  • Rui Gao
    • Categories Stochastic Programming

    We study convergence properties of the discrete-time Mean-Field Langevin Stochastic Descent-Ascent (MFL-SDA) algorithm for solving distributional minimax optimization. These problems arise in various applications, such as zero-sum games, generative adversarial networks and distributionally robust learning. Despite the significance of MFL-SDA in these contexts, the discrete-time convergence rate remains underexplored. To address this gap, we establish … 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