Qi Zhang

Enhancing explainability of stochastic programming solutions via scenario and recourse reduction

  • Tushar Rathi
  • Rishabh Gupta
  • Jose M. Pinto
  • Qi Zhang
    • Categories Stochastic Programming Tags , , ,

    Stochastic programming (SP) is a well-studied framework for modeling optimization problems under uncertainty. However, despite the significant advancements in solving large SP models, they are not widely used in industrial practice, often because SP solutions are difficult to understand and hence not trusted by the user. Unlike deterministic optimization models, SP models generally involve recourse … 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

    Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization

  • Rishabh Gupta
  • Qi Zhang
    • Categories Applications - OR and Management Sciences, Integer Programming, Linear, Cone and Semidefinite Programming Tags , ,

    This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program. Specifically, we consider the data-driven setting in which the available data are noisy observations of optimal solutions that correspond to different instances of the linear program. We introduce a new formulation of the problem that, … 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