Approximating optimal threshold values for unreliable manufacturing systems via stochastic optimization

Author

Yan, Houmin ; Yin, G. ; Lou, Sheldon X C

Author_Institution

Fac. of Manage., Toronto Univ., Ont., Canada

fYear

1992

fDate

1992

Firstpage

1657

Abstract

The algorithms proposed utilize perturbation analysis to carry out gradient estimation and stochastic approximation to find the optimal threshold values for unreliable one- and two-machine systems. The perturbation analysis techniques initiated by Y.C. Ho and X. Cao (1991) are used to deduce a simple gradient estimate, and the stochastic optimization techniques are employed to develop iterative algorithms for approximating the optimal threshold values. The formulation for the one-machine problem is given and the iterative algorithm is also developed. An example for the one-machine case is included. The result from the numerical study is compared with existing analytical results. The extension to multimachine systems is explained

Keywords

conjugate gradient methods; perturbation techniques; production control; stochastic processes; gradient estimate; gradient estimation; iterative algorithms; multimachine systems; one-machine systems; optimal threshold values; perturbation analysis; stochastic optimization; two-machine systems; unreliable manufacturing systems; Algorithm design and analysis; Approximation algorithms; Exponential distribution; Interference; Iterative algorithms; Manufacturing; Manufacturing systems; Optimal control; Optimization methods; Production control; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371148

Filename

371148