Controlled Markov Chains with Weak and Strong Interactions: Asymptotic Optimality and Applications to Manufacturing

Q. Zhang, G. Yin and E. K. Boukas
Additional contact information
Q. Zhang: University of Georgia
G. Yin: Wayne University
E. K. Boukas: École Polytechnique de Montréal and GERAD

Journal of Optimization Theory and Applications, 1997, vol. 94, issue 1, No 11, 169-194

Abstract: Abstract This paper deals with the asymptotic optimality of a stochastic dynamic system driven by a singularly perturbed Markov chain with finite state space. The states of the Markov chain belong to several groups such that transitions among the states within each group occur much more frequently than transitions among the states in different groups. Aggregating the states of the Markov chain leads to a limit control problem, which is obtained by replacing the states in each group by the corresponding average distribution. The limit control problem is simpler to solve as compared with the original one. A nearly-optimal solution for the original problem is constructed by using the optimal solution to the limit problem. To demonstrate, the suggested approach of asymptotic optimal control is applied to examples of manufacturing systems of production planning.

Keywords: Stochastic optimal control; singularly perturbed Markov chains; weak and strong interactions; piecewise-deterministic processes; aggregated Markov chains; stationary distributions; HJB equation; flexible manufacturing; production planning (search for similar items in EconPapers)
Date: 1997
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Citations: View citations in EconPapers (1)

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DOI: 10.1023/A:1022667905086

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