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Stochastic control

Broadly, this area concerns determination of optimal policies/strategies for control of systems that evolve in a random environment. 

Markov decision processes (MDP) 

Current interest in Markov decision processes looks at sensitivity analysis of optimal policies w.r.t. various parameters like unit costs and transition probabilities of the model. A related topic is reinforcement learning algorithms of such models. The motivation of both these strands of work is to better our understanding regarding model uncertainty. 

Controlled diffusion 

Current interest in control diffusion equations is to develop the theory of degenerate diffusion equations; a technically difficult topic. We also intend to look at applications in finance of such models. 

Simulation Modeling and Analysis 

Simulation techniques are becoming essential in the modeling and analysis of complex systems. Research works are being carried out in the field of Discrete-Event Simulation, continuous System Dynamics simulation and Hybrid modeling. Primary areas of application are in manufacturing systems and supply chains, with potential applications in other areas. Framework and infrastructure for enabling distributed simulation is also developed using High Level Architecture (HLA) and Web Services. 

Recent and ongoing works include production and distribution planning in supply chains using hybrid models; simulation-optimization; interface specifications and statistical data analysis in distributed simulation; control theoretic analysis of supply chains; dynamics of internal and external disturbances on supply chains behavior. 

Faculty: Jayendran Venkateswaran