Adaptive control of stochastic manufacturing systems with hidden Markovian demands and small noise

Author

Duncan, T.E. ; Pasik-Duncan, B. ; Zhang, Q.

Author_Institution

Dept. of Math., Kansas Univ., Lawrence, KS, USA

Volume

44

Issue

2

fYear

1999

fDate

2/1/1999 12:00:00 AM

Firstpage

427

Lastpage

430

Abstract

The adaptive production planning of failure-prone manufacturing systems is considered. In real manufacturing systems, the product demand is usually not known a priori. One of the major tasks in production scheduling is to estimate and predict the demand. In this paper, the authors consider the demand to be either the sum of an unknown rate and a small white noise or the sum of a hidden Markov chain and a small white noise. An algorithm is given to define a family of estimates for the unknown demand processes. Based on this family of estimates, adaptive controls are constructed, which are shown to be nearly optimal

Keywords

Markov processes; adaptive control; identification; production control; white noise; adaptive production planning; failure-prone manufacturing systems; hidden Markov chain; hidden Markovian demands; product demand; production scheduling; small noise; stochastic manufacturing systems; unknown demand processes; Adaptive control; Hidden Markov models; Manufacturing systems; Mathematics; Parameter estimation; Production planning; Stochastic processes; Stochastic resonance; Stochastic systems; White noise;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.746283

Filename

746283