We study the impact of backbones in optimization and approximation problems. We show that some optimization problems like graph coloring resemble decision problems, with problem h...
Abstract. A well studied and difficult class of scheduling problems concerns parallel machines and precedence constraints. In order to model more realistic situations, we consider ...
This paper develops simple approximate methods to analyze a two-stage stochastic distribution system consisting of one warehouse and multiple retailers. We consider local and cent...
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democra...
Fusun Yaman, Thomas J. Walsh, Michael L. Littman, ...