16 citations · 27 across the 7 of their papers we have counts for
7 papers
Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls
Ruiwei Jiang, Minseok Ryu, Guanglin Xu
We study a single-server appointment scheduling problem with a fixed sequence of appointments, for which we must determine the arrival time for each appointment. We specifically ex…
An Integer Programming Formulation of the Key Management Problem in Wireless Sensor Networks
Guanglin Xu, Alexander Semenov, Maciej Rysz
With the advent of modern communications systems, much attention has been put on developing methods for securely transferring information between constituents of wireless sensor ne…
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions
Xi Chen, Qihang Lin, Guanglin Xu
Distributionally robust optimization (DRO) has been introduced for solving stochastic programs where the distribution of the random parameters is unknown and must be estimated by s…
Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming
Guanglin Xu, Grani A. Hanasusanto
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, t…
A Data-Driven Distributionally Robust Bound on the Expected Optimal Value of Uncertain Mixed 0-1 Linear Programming
Guanglin Xu, Samuel Burer
This paper studies the expected optimal value of a mixed 0-1 programming problem with uncertain objective coefficients following a joint distribution. We assume that the true distr…
A Copositive Approach for Two-Stage Adjustable Robust Optimization with Uncertain Right-Hand Sides
Guanglin Xu, Samuel Burer
We study two-stage adjustable robust linear programming in which the right-hand sides are uncertain and belong to a convex, compact uncertainty set. This problem is NP-hard, and th…
Robust Sensitivity Analysis of the Optimal Value of Linear Programming
Guanglin Xu, Samuel Burer
We propose a framework for sensitivity analysis of linear programs (LPs) in minimization form, allowing for simultaneous perturbations in the objective coefficients and right-hand…