Optimization and Performance Evaluation of Network Models

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ECS-9972957MeynThe proposed research will consider the development of recent approaches to optimal control, reinforceraent learning, and performance evaluation for complex systems. The methods to be employed are based upon recent work conducted by the PI on Markov chain stability theory, linear programming methods, and fluid model approximation.Research on dynamic optimization will be based on a recent discovery of the PI which establishes a relationship between the optimal control of a stochastic model, and the optimal control of a simpler 'leaky bucket' fluid model. The latter gives rise to a constrained L1 optimal control problem which can be solved in many practical examples.The optimization theory will be improved through a deeper analysis of linear programming methods for approximating the value function in the optimal control problem of interest.Reinforcement learning algorithms will be considered for policy improvement in stochastic models. The PI will again base the analysis on fluid model approximations.Specific applications include the development of scheduling and routing algorithms for multiclass queueing networks. The algorithms will be based upon translations of fluid policies; tranlations of fluid value function approximations; and the application of reinforcement learning techniques.