In this paper, we propose an extended local search framework to solve combinatorial optimization problems with data uncertainty. Our approach represents a major departure from sce...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
Abstract. Motivated by an application in project portfolio analysis under uncertainty, we develop an algorithm S-VNS for solving stochastic combinatorial optimization (SCO) problem...
Walter J. Gutjahr, Stefan Katzensteiner, Peter Rei...
We present a new technique (RIIPS) for solving rostering problems in the presence of service uncertainty. RIIPS stands for "Rostering by Iterating Integer Programming and Sim...
—In this paper∗ , we devise a novel method for bottleneck analysis of UDP networks based on the concept of network utility maximization. To determine the losses on the links in...