Grani A. Hanasusanto

Distributionally Robust Optimization via Targeted Integral Probability Metrics for General Data Processes

  • Lanran Fang
  • Jianqiang Cheng
  • Grani A. Hanasusanto
  • Yijie Wang
    • Categories Optimization in Data Science, Robust Optimization, Stochastic Programming Tags , , , , , ,

    Distributionally robust optimization (DRO) provides a principled framework for decision-making under distributional uncertainty. Existing classical data-driven DRO frameworks typically construct ambiguity sets from distributional information, such as moment constraints, divergence neighborhoods, or Wasserstein balls, specified before the downstream loss is considered. We propose a task-aware DRO framework based on targeted integral probability metrics. The ambiguity … Read more

    Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions

  • YoungChul Yoon
  • Grani A. Hanasusanto
  • Yijie Wang
    • Categories Robust Optimization, Stochastic Programming Tags , , , , ,

    Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant challenges when dealing with multimodal or mixtures of distributions. The inherent complexity of such structures often precludes an explicit functional relationship between the contextual information and the uncertain parameters, limiting the direct applicability of parametric … Read more

    Distributionally Robust Optimization with Decision-Dependent Information Discovery

  • Qing Jin
  • Angelos Georghiou
  • Phebe Vayanos
  • Grani A. Hanasusanto
    • Categories Robust Optimization Tags , , , , ,

    We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are revealed only if an (often costly) investment is made in the first stage. This class of problems finds many important applications in selection problems (e.g., in hiring, project portfolio optimization, or optimal sensor location). … Read more

    Distributionally Fair Stochastic Optimization using Wasserstein Distance

  • Ye Qing
  • Grani A. Hanasusanto
  • Weijun Xie
    • Categories Stochastic Programming Tags , ,

    A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a set of constraints. However, implementing such optimization-based decisions can have varying impacts on individuals, and when assessed using the individuals’ utility functions, these impacts may differ substantially across demographic … Read more

    Data-Driven Stochastic Dual Dynamic Programming: Performance Guarantees and Regularization Schemes

  • Hyuk Park
  • Zhuangzhuang Jia
  • Grani A. Hanasusanto
    • Categories Stochastic Programming Tags , , ,

    We propose a data-driven extension of the stochastic dual dynamic programming (SDDP) algorithm for multistage stochastic linear programs under a continuous-state, non-stationary Markov data process. Unlike traditional SDDP methods—which often assume a known probability distribution, stagewise independent data process, or uncertainty restricted to the right-hand side of constraints—our approach overcomes these limitations, making it more … Read more

    Linearizing Bilinear Products of Shadow Prices and Dispatch Variables in Bilevel Problems for Optimal Power System Planning

  • Grani A. Hanasusanto
  • Nicholas Laws
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Energy Tags , , , ,

    This work presents a general method for linearizing bilinear terms in the upper level of bilevel optimization problems when the bilinear terms are products of the primal and dual variables of the lower level. Bilinear terms of this form often appear in energy market optimization models where the dual variable represents the market price of … Read more

    Transmission Switching Under Wind Uncertainty Using Linear Decision Rules

  • Grani A. Hanasusanto
  • Yuqi Zhou
  • Hao Zhu
    • Categories Applications - Science and Engineering Tags , , ,

    Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal transmission switching (OTS) problem for determining the generation dispatch and network topology that can account for uncertain energy resources. To efficiently solve the resultant two-stage stochastic program, we propose a tractable linear … Read more

    Optimal Residential Battery Storage Operations Using Robust Data-driven Dynamic Programming

  • Grani A. Hanasusanto
  • Benjamin Leibowicz
  • Nan Zhang
    • Categories Applications - Science and Engineering Tags , , , , ,

    In this paper, we consider the problem of operating a battery storage unit in a home with a rooftop solar photovoltaic (PV) system so as to minimize expected long-run electricity costs under uncertain electricity usage, PV generation, and electricity prices. Solving this dynamic program using standard techniques is computationally burdensome, and is often complicated by … Read more

    On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach

  • Grani A. Hanasusanto
  • Chin Pang Ho
    • Categories Stochastic Programming

    We consider generic stochastic optimization problems in the presence of side information which enables a more insightful decision. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. A decision maker who adapts her decisions according to the observed side information solves an optimization problem where the … Read more

    Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming

  • Grani A. Hanasusanto
  • Guanglin Xu
    • Categories Dynamic Programming, Linear, Cone and Semidefinite Programming, Robust Optimization

    We study decision rule approximations for generic multi-stage robust linear optimization problems. We consider linear decision rules for the case when the objective coefficients, the recourse matrices, and the right-hand sides are uncertain, and consider quadratic decision rules for the case when only the right-hand sides are uncertain. The resulting optimization problems are NP-hard but … Read more