Title :
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
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;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371148
