Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Valuing the flexibility of flexible manufacturing systems with fast decision rules

  • 2 Modification Tasks
  • Conference paper
  • First Online:

Abstract

We compare the use of stochastic dynamic programming (SDP), Neural Networks and a simple approximation rule for calculating the real option value of a flexible production system. While SDP yields the best solution to the problem, it is computationally prohibitive for larger settings. We test two approximations of the value function and show that the results are comparable to those obtained via SDP. These methods have the advantage of a high computational performance and of no restrictions on the type of process used. Our approach is not only useful for supporting large investment decisions, but it can also be applied in the case of routine decisions like the determination of the production program when stochastic profit margins occur.

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

Access this chapter

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aarts, E.H.L. and Korst, J.,Simulated Annealing and Boltzmann Machines. Wiley, 1989.

    Google Scholar 

  2. Dixit, A., Pindyck, R.,Investment Under Uncertainty, Princeton, 1993.

    Google Scholar 

  3. Geske R. and Shastri K., Valuation by Approximation: A Comparison of Alternative Valuation Techniques,Journal of Financial and Quantitative Analysis, 20 (1), 45–71, March 1985.

    Google Scholar 

  4. Jones, R.A. and J.M. Ostroy, Flexibility and Uncertainty,Rev. Econ. Studies, vol. LI, pp. 13–32, 1984.

    Google Scholar 

  5. Kamrad B., A Lattice Claims Model for Capital Budgeting,IEEE Transactions on Engineering Management, 42, 140–149, 1995.

    Google Scholar 

  6. Kulatilaka N., Valuing the Flexibility of Flexible Manufacturing Systems,IEEE Transactions on Engineering Management, Vol. 35, 250–257, 1988.

    Google Scholar 

  7. Kulatilaka N., The Value of Flexibility: A General Model of Real Options. In: L. Trigeorgis, editor,Real Options in Capital Investment — Models, Strategies, and Applications, Praeger, 87–107, 1995.

    Google Scholar 

  8. Kulatilaka, N., Operating Flexibilities in Capital Budgeting: Substitutability and Complementarity. In L. Trigeorgis, editor,Real Options in Capital Investment — Models, Strategies, and Applications, Praeger, 121–132, 1995.

    Google Scholar 

  9. Kirkpatrick, S., C.D. Gelatt, and M.P. Vecchi, Optimization by Simulated Annealing.Science, Vol. 220, No. 4598, 671–680, 1983.

    Google Scholar 

  10. Lint, O., Real Options Analysis in Advanced Manufacturing Technologies,International Journal of Computer Integrated Manufacturing, 145–152, 1992.

    Google Scholar 

  11. Mitchell, G.R., Alternative frameworks for technology strategy,European Journal of Operations Research, Nr. 47, 153–161, 1990.

    Google Scholar 

  12. Ripley, B.D., Statistical Aspects of Neural Networks. In: Barndorff-Nielsen, O.E., Jensen, J.L. and Kendall, W.S., editors,Networks and Chaos — Statistical and Probabilistic Aspects. London, Chapman & Hall, 1993.

    Google Scholar 

  13. White, H., Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings.Working Paper, Department of Economics, University of California, San Diego, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Information Processing, Vienna University of Economics & Business Administration, Pappenheimgasse 35, A-1200, Vienna, Austria

    Markus Feurstein & Martin Natter

Authors
  1. Markus Feurstein

    You can also search for this author inPubMed Google Scholar

  2. Martin Natter

    You can also search for this author inPubMed Google Scholar

Editor information

Angel Pasqual del Pobil José Mira Moonis Ali

Rights and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Feurstein, M., Natter, M. (1998). Valuing the flexibility of flexible manufacturing systems with fast decision rules. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_401

Download citation

Publish with us

[8]ページ先頭
©2009-2025 Movatter.jp