Stochastic Modeling and Simulation Research
Risk and uncertainty is inherent in all real-world systems, and understanding its impact is essential in performance analysis and optimization. Researchers in the IEOR Department at UC Berkeley are developing stochastic models and simulations for applications ranging from call centers to cloud computing, as well as expanding fundamental theory in areas such as stochastic control, semi-Martingale and filtration expansions, the economics of queueing systems, and design of simulation experiments.
R. Righter, “The Stochastic Sequential Assignment Problem with Arrivals,” Probability in the Engineering and Informational Sciences, 2011.
J.-H. Kim, H.-S. Ahn and R. Righter, “Optimal Production Policies with Multistage Stochastic Leadtimes,” Probability in the Engineering and Informational Sciences, vol. 23, pp. 515-543, 2009.
X. Guo, J. Liu, and X. Y. Zhou. A constrained non-linear regular-singular stochastic control problem, with applications, Stochastic Processes and their Applications, 109(2):167-187, 2004.