Optimization of reliability allocation strategies through use of genetic algorithms

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This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of ... continued below

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11 p.

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Campbell, J.E. & Painton, L.A. August 1996.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 14 times . More information about this article can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.

Physical Description

11 p.

Notes

OSTI as DE96013823

Source

  • 6. American Institute of Aeronautics and Astronautics (AIAA)/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization, Bellevue, WA (United States), 4-6 Sep 1996

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  • Other: DE96013823
  • Report No.: SAND--96-1998C
  • Report No.: CONF-9609208--1
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 281882
  • Archival Resource Key: ark:/67531/metadc670852

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • August 1996

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  • June 29, 2015, 9:42 p.m.

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  • April 14, 2016, 9:28 p.m.

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Campbell, J.E. & Painton, L.A. Optimization of reliability allocation strategies through use of genetic algorithms, article, August 1996; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc670852/: accessed October 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.