Skip to main content

Decision Analysis in Management: Methods and Models

  • Chapter
Optimal Decisions Under Uncertainty

Part of the book series: Universitext ((UTX))

  • 463 Accesses

Abstract

Decision analysis refers to the various operational methods used by management for the efficient running of an enterprise. Very broadly viewed it may involve considerations of long range corporate planning and the suitability of alternative organization structures; very narrowly it may specify a linear programming model to determine an optimal output-mix which maximizes company profits. From a practical standpoint the decision analysis in management science and operations research deal with three broad groups of methods or models:

  1. A.

    Optimization models usually under constraints e.g. linear programming (LP) model.

  2. B.

    Stochastic systems usually with no explicit optimizing objective e.g., queing models.

  3. C.

    Stochastic optimization in a dynamic system e.g., production-inventory control problem over time.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sengupta, J.K. Decision Models in Stochastic Programming. North Holland: Amsterdam, 1982.

    Google Scholar 

  2. Kolbin, V.V. Stochastic Programming. Reidel Publishing: Dordrecht, Holland, 1977.

    Google Scholar 

  3. Owen, G. Game Theory. Academic Press: New York, 1982.

    Google Scholar 

  4. Hillier, F.S. and G.L. Lieberman. Operations Research. Holden-Day: San Francisco,

    Google Scholar 

  5. Baumol, W.J. Economic Theory and Operations Analysis. Prentice Hall: Englewood Cliffs, New Jersey, 1977.

    Google Scholar 

  6. Bunn, D. Analysis for Optimal Decisions. New York: John Wiley, 1982.

    Google Scholar 

  7. Zukhovitskiy, S.I. and L.I. Avdeyeva. Linear and Convex Programming. London: W.B. Saunders, 1966.

    Google Scholar 

  8. Howard, R.A. Dynamic Programming and Markov Processes. Cambridge, Massachusetts: MIT Press, 1960.

    Google Scholar 

  9. Vajda, S. Probabilistic Programming. New York: Academic ress, 1972.

    Google Scholar 

  10. Lange, O. Optimal Decisions: Principles of Programming. Oxford: Pergamon Press, 1971.

    Google Scholar 

  11. Murata, Y. Optimal Control Methods for Linear Discrete-Time Economic Systems. New York: Springer-Verlag, 1982.

    Book  Google Scholar 

  12. Sengupta, J.K. Information and Efficiency in Economic Decisions. Hague ( The Netherlands ): Martinus Nijhoff Publishers, 1984.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1985 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sengupta, J.K. (1985). Decision Analysis in Management: Methods and Models. In: Optimal Decisions Under Uncertainty. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-70163-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-70163-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-15032-9

  • Online ISBN: 978-3-642-70163-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics