Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans | Journal of Intelligent Manufacturing Skip to main content
Log in

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

  • Papers
  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In manufacturing industries, sampling inspection is a common practice for quality assurance and cost reduction. The basic decisions in sampling inspection are how many manufactured items to be sampled from each lot and how many identified defective items in the sample to accept or reject each lot. Because of the combinatorial nature of alternative solutions on the sample sizes and acceptance criteria, the problem of determining an optimal sampling plan is NP-complete. In this paper, a neurally-inspired approach to generating acceptance sampling inspection plans is proposed. A Bayesian cost model of multi-stage-multi-attribute sampling inspections for quality assurance in serial production systems is formulated. This model can accommodate various dispositions of rejected lott such as scraping and screening. The model also can reflect the relationships between stages and among attributes. To determine the sampling plans based on the formulated model, a neurally-inspired stochastic algorithm is developed. This algorithm simulates the state transition of a primal-dual stochastic neural network to generate the sampling plans. The simulated primal network is responsible for generation of new states whereas the dual network is for recording the generated solutions. Starting with an arbitrary feasible solution, this algorithm is able to converge to a near optimal or an optimal sampling plan with a sequence of monotonically improved solutions. The operating characteristics and performance of the algorithm are demonstratedvia numerical examples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aarts, E. H. and Korst, J. H. (1989) Boltzmann machines for traveling salesman problem.European Journal of Operational Research,36, 29–95.

    Google Scholar 

  • Case, K., Schmidt, J. and Bennett, G. (1975) A discrete economic multiattribute acceptance sampling.AIIE Transactions,7, 363–9.

    Google Scholar 

  • Guenther, W. (1971) On the determination of single sampling acceptance plans based upon a linear cost model and a prior distribution.Technometrics,13, 483–98.

    Google Scholar 

  • Hald, A. (1960) The compound hypergeometric distribution and a system of single sampling inspection plans based on prior distribution and costs.Technometrics,2, 275–340.

    Google Scholar 

  • Hald, A. (1968) Bayesian single sample attribute plans for continuous prior distributions.Technometrics,10, 667–83.

    Google Scholar 

  • Hinton, G. E., Sejnowski, T. J. and Ackley, D. H. (1984) Boltzmann machines: constraint satisfaction networks that learn, inTechnical Report CMU-CS-84-119, Carnegie Mellon University, Pittsburgh.

    Google Scholar 

  • Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities.Proceedings of National Academy of Sciences, USA, Biophysics,79, 2554–8.

    Google Scholar 

  • Hopfield, J. J. (1984) Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of National Academy of Sciences, USA, Biophysics,81, 3088–92.

    Google Scholar 

  • Hopfield, J. J. and Tank, D. W. (1985) ‘Neural’ computation of decisions in optimization problems,Biological Cybernetics,52, 141–52.

    Google Scholar 

  • Hsu, J. (1984) A hybrid inspection system for the multistage production process.International Journal of Production Research,22, 63–9.

    Google Scholar 

  • Hurst, E. (1973) Imperfect inspection in multistage production process.Management Science,30, 378–84.

    Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P. (1983) Optimization by simulated annealing.Science,220, 671–80.

    Google Scholar 

  • Lindsay, G. and Bishop, A. (1964) Allocation of screening inspection effort—a dynamic programming approach.Management Science,10, 342–52.

    Google Scholar 

  • Malakooti, B. and Balhorn, W. H. (1987) Selection of acceptance sampling plans with multi-attribute defects in computeraided quality control.International Journal of Production Research,25, 869–87.

    Google Scholar 

  • Moskowitz, H. and Berry, W. (1976) A Bayesian algorithm for determining optimal single sample acceptance plans for product attributes.Management Science,22, 1238–50.

    Google Scholar 

  • Moskowitz, H. and Plante, R. (1984) Effect of risk aversion on single sample attribute inspection plans.Management Science,30, 1226–37.

    Google Scholar 

  • Moskowitz, H., Plante, R., Ranvindran, A. and Tang, K. (1984) Multiattribute Bayesian acceptance sampling plans for screening and scraping rejected lots.IIE Transactions,6, 185–92.

    Google Scholar 

  • Moskowitz, H., Ranvindran, A., Klein, G. and Eswaran, P. (1982) A bicriterion model for acceptance sampling.TIMS/Studies in Management Sciences,19, 305–22.

    Google Scholar 

  • Moskowitz, H., Ranvindran, A. and Patton, J. (1979) An algorithm for selection an optimal acceptance plan in quality control and auditing.International Journal of Production Research,17, 581–94.

    Google Scholar 

  • Pruzan, P. (1967) A dynamic programming approach in production line inspection.Technometrics,9, 73–81.

    Google Scholar 

  • Schmidt, J. and Bennett, G. (1972) Economic multiattribute acceptance sampling. AIIE Transactions,4, 194–9.

    Google Scholar 

  • Schmidt, J. and Taylor, R. (1973) A dual purpose of cost based quality control system.Technometrics,15, 151–66.

    Google Scholar 

  • Tang, K., Plante, R. and Moskowitz, H. (1986) Multiattribute Bayesian acceptance sampling plans under nondestructive inspection.Management Science,32, 739–50.

    Google Scholar 

  • Wang, J. (1990) A parallel distributed processor for the quadratic assignment problem, inProceedings of the International Neural Network Conference, pp. 278–81.

  • Xu, L. (1990) An improvement on simulated annealing and boltzmann machine inProceedings of International Joint Conference on Neural Networks,1, 341–4.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, J., Chankong, V. Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans. J Intell Manuf 2, 327–336 (1991). https://doi.org/10.1007/BF01471180

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01471180

Keywords

Navigation