Melvyn Sim

A Tractable Approximation of Stochastic Programming via Robust Optimization

  • Xin Chen
  • Peng Sun
  • Jiawei Zhang
  • Melvyn Sim
    • Categories Robust Optimization, Second-Order Cone Programming, Stochastic Programming Tags , , ,

    Stochastic programming, despite its immense modeling capabilities, is well known to be computationally excruciating. In this paper, we introduce a unified framework of approximating multiperiod stochastic programming from the perspective of robust optimization. Specifically, we propose a framework that integrates multistage modeling with safeguarding constraints. The framework is computationally tractable in the form of second … Read more

    A Robust Optimization Framework for Analyzing Distribution Systems with Transshipment

  • Mabel C. Chou
  • Melvyn Sim
  • Rick So
    • Categories Robust Optimization, Stochastic Programming, Supply Chain Management Tags , , ,

    This paper studies a distribution system consisting of multiple retail locations with transshipment operations among the retailers. Due to the difficulty in computing the optimal solution imposed by the transshipment operations and in estimating shortage cost from a practical perspective, we propose a robust optimization framework for analyzing the impact of transshipment operations on such … Read more

    Constructing Risk Measures from Uncertainty Sets

  • Karthik Natarajan
  • Dessislava Pachamanova
  • Melvyn Sim
    • Categories Finance and Economics, Robust Optimization Tags ,

    We propose a unified theory that links uncertainty sets in robust optimization to risk measures in portfolio optimization. We illustrate the correspondence between uncertainty sets and some popular risk measures in finance, and show how robust optimization can be used to generalize the concepts of these measures. We also show that by using properly defined … Read more

    A Robust Optimization Perspective of Stochastic Programming

  • Xin Chen
  • Peng Sun
  • Melvyn Sim
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for bounded random variables known as the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. We also propose a tractable robust optimization approach for obtaining robust solutions … Read more