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Title: A Multiobjective Optimization Framework for Stochastic Control of Complex Systems

This paper addresses the problem of minimizing the long-run expected average cost of a complex system consisting of subsystems that interact with each other and the environment. We treat the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and we show that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. For practical situations with constraints consistent to those we study here, our results imply that the Pareto control policy may be of value in deriving online an optimal control policy in complex systems.
 [1] ;  [1] ;  [2]
  1. ORNL
  2. The University of Tennessee
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Conference: The 2015 American Control Conference, Chicago, IL, USA, 20150701, 20150703
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Complex systems; stochastic optimal control; controlled Markov chains; multicriteria optimization