DocumentCode :
2098962
Title :
Controlled Markov processes on the infinite planning horizon: weighted and overtaking cost criteria
Author :
Fernández-Gaucherand, Emmanuel ; Ghosh, Mrinal K. ; Marcus, Steven L.
Author_Institution :
Dept. of Syst. & Ind. Eng., Arizona Univ., Tucson, AZ, USA
fYear :
1993
fDate :
15-17 Dec 1993
Firstpage :
381
Abstract :
Stochastic control problems for controlled Markov processes models with an infinite planning horizon are considered, under some non-standard cost criteria. The classical discounted and average cost criteria can be viewed as complementary, in the sense that the former captures the short-time and the latter the long-time performance of the system. Thus, the authors study a cost criterion obtained as weighted combinations of these criteria, extending to a general state and control space framework several results by Feinberg and Shwartz (1992), and by Krass et al. (1992). In addition, a functional characterization is given for overtaking optimal policies, for problems with countable state spaces and compact control spaces; the authors´ approach is based on qualitative properties of the optimality equation for problems with an average cost criterion
Keywords :
Markov processes; stochastic systems; average cost criteria; compact control spaces; controlled Markov processes; countable state spaces; discounted cost criteria; functional characterization; infinite planning horizon; nonstandard cost criteria; optimality equation; overtaking cost criteria; overtaking optimal policies; qualitative properties; weighted criteria; Cost function; Equations; Markov processes; Operations research; Optical wavelength conversion; Optimal control; Process control; Process planning; State-space methods; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
Type :
conf
DOI :
10.1109/CDC.1993.325124
Filename :
325124
Link To Document :
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