Miguel A. Lejeune

Distributionally Robust Optimization under Decision-Dependent Ambiguity Set with an Application to Machine Scheduling

  • Miguel A. Lejeune
  • Nilay Noyan
  • Gabor Rudolf
    • Categories Scheduling, Stochastic Programming

    We introduce a new class of distributionally robust optimization problems under decision dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the … Read more

    A Framework for Solving Chance-Constrained Linear Matrix Inequality Programs

  • Jianqiang Cheng
  • Miguel A. Lejeune
  • Roya Karimi
    • Categories Stochastic Programming Tags , , , ,

    We propose a novel partial sample average approximation (PSAA) framework to solve the two main types of chance-constrained linear matrix inequality (CCLMI) problems: CCLMI with random technology matrix, and CCLMI with random right-hand side. We propose a series of computationally tractable PSAA-based approximations for CCLMI problems, analyze their properties, and derive sufficient conditions ensuring convexity. … Read more

    Distributionally Robust Optimization with Decision-Dependent Ambiguity Set

  • Miguel A. Lejeune
  • Nilay Noyan
  • Gabor Rudolf
    • Categories Stochastic Programming

    We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main … Read more

    Data-Driven Distributionally Robust Chance-Constrained Optimization with Wasserstein Metric

  • Ran Ji
  • Miguel A. Lejeune
    • Categories Robust Optimization, Second-Order Cone Programming, Stochastic Programming Tags , ,

    We study distributionally robust chance-constrained programming (DRCCP) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to distributionally robust optimization problems subjected to individual as well as joint chance constraints, with random right-hand side and technology vector, and under two types of uncertainties, called uncertain probabilities and continuum of realizations. … Read more

    Risk-based Loan Pricing: Portfolio Optimization Approach With Marginal Risk Contribution

  • So Yeon Chun
  • Miguel A. Lejeune
    • Categories (Mixed) Integer Nonlinear Programming, Finance and Economics, Stochastic Programming Tags , , , , , , ,

    We consider a lender (bank) who determines the optimal loan price (interest rates) to offer to prospective borrowers under uncertain risk and borrowers’ response. A borrower may or may not accept the loan at the price offered, and in the presence of default risk, both the principal loaned and the interest income become uncertain. We … Read more

    Data-Driven Optimization of Reward-Risk Ratio Measures

  • Ran Ji
  • Miguel A. Lejeune
    • Categories Finance and Economics, Stochastic Programming Tags , , , , ,

    We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a … Read more

    Regularized Stochastic Dual Dynamic Programming for convex nonlinear optimization problems

  • Vincent Guigues
  • Miguel A. Lejeune
  • Wajdi Tekaya
    • Categories Dynamic Programming, Finance and Economics, Stochastic Programming Tags , , , ,

    We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regularization of the Stochastic Dual Dynamic Programming (SDDP) … Read more

    Public Facility Location Using Dispersion, Population, and Equity Criteria

  • Rajan Batta
  • Miguel A. Lejeune
  • Srinivas Prasad
    • Categories Facility Planning and Design, Production and Logistics Tags , ,

    Administrators/Decision Makers (DMs) responsible for making locational decisions for public facilities have many other overriding factors to consider that dominate traditional OR/MS objectives that relate to response time. We propose that an appropriate role for the OR/MS analyst is to help the DMs identify a good set of solutions rather than an optimal solution that … Read more

    Effectiveness-Equity Models for Facility Location Problems on Tree Networks

  • Miguel A. Lejeune
  • Srinivas Prasad
    • Categories Convex Optimization, Multi-Criteria Optimization, Production and Logistics

    We propose models to investigate effectiveness-equity tradeoffs in tree network facility location problems. We use the commonly used median objective as a measure of effectiveness, and the Gini index as a measure of (in)equity, and formulate bicriteria problems involving these objectives. We develop procedures to identify an efficient set of solutions to these problems, analyze … Read more

    Stochastic Network Design for Disaster Preparedness

  • Xing Hong
  • Miguel A. Lejeune
  • Nilay Noyan
    • Categories Applications - Science and Engineering, Stochastic Programming

    We propose a new stochastic modeling approach for a pre-disaster relief network design problem under uncertain demand and transportation capacities. We determine the size and the location of the response facilities and the inventory levels of relief supplies at each facility with the goal of guaranteeing a certain level of network reliability. The overall objective … Read more