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Timing replacement decisions under discontinuous technological change

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  • Wallace J. Hopp
  • Suresh K. Nair

Abstract

We consider the problem of deciding whether to keep a piece of equipment or replace it with a more advanced technology in an environment of technological change. Our model assumes that the costs associated with the presently available technology and future technologies are known, but that the appearance times of future technologies are uncertain. We develop a procedure for computing the optimal keep‐or‐replace decision that iteratively incorporates a technological forecast. For a certain class of situations, we show that our approach requires the minimum possible amount of forecasted data.

Suggested Citation

  • Wallace J. Hopp & Suresh K. Nair, 1991. "Timing replacement decisions under discontinuous technological change," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(2), pages 203-220, April.
    • Handle: RePEc:wly:navres:v:38:y:1991:i:2:p:203-220
    DOI: 10.1002/1520-6750(199104)38:23.0.CO;2-E
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    References listed on IDEAS

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    1. Wallace J. Hopp, 1989. "Technical Note—Identifying Forecast Horizons in Nonhomogeneous Markov Decision Processes," Operations Research, INFORMS, vol. 37(2), pages 339-343, April.
    2. Suresh Chand & Suresh Sethi, 1982. "Planning horizon procedures for machine replacement models with several possible replacement alternatives," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 29(3), pages 483-493, September.
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    7. Tzvi Goldstein & Shaul P. Ladany & Abraham Mehrez, 1988. "A discounted machine‐replacement model with an expected future technological breakthrough," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(2), pages 209-220, April.
    8. Y. S. Sherif & M. L. Smith, 1981. "Optimal maintenance models for systems subject to failure–A Review," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 28(1), pages 47-74, March.
    9. C. Bes & S. P. Sethi, 1988. "Concepts of Forecast and Decision Horizons: Applications to Dynamic Stochastic Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 295-310, May.
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    Cited by:

    1. Natali Hritonenko & Yuri Yatsenko, 2012. "Fleet replacement under technological shocks," Annals of Operations Research, Springer, vol. 196(1), pages 311-331, July.
    2. A. Bensoussan & Q. Feng & S. P. Sethi, 2022. "Integrating equipment investment strategy with maintenance operations under uncertain failures," Annals of Operations Research, Springer, vol. 317(2), pages 353-386, October.
    3. Israel David & Eitan Greenshtein & Avraham Mehrez, 1997. "A dynamic‐programming approach to continuous‐review obsolescent inventory problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(8), pages 757-774, December.
    4. Joseph C. Hartman, 2000. "The parallel replacement problem with demand and capital budgeting constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(1), pages 40-56, February.
    5. Ali Dogramaci & Nelson M. Fraiman, 2004. "Replacement Decisions with Maintenance Under Uncertainty: An Imbedded Optimal Control Model," Operations Research, INFORMS, vol. 52(5), pages 785-794, October.
    6. Miles Gietzmann & Adam Ostaszewski, 2004. "Predicting firm value: the superiority of -theory over residual income," Accounting and Business Research, Taylor & Francis Journals, vol. 34(4), pages 349-377.
    7. Awi Federgruen & Michal Tzur, 1996. "Detection of minimal forecast horizons in dynamic programs with multiple indicators of the future," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(2), pages 169-189, March.
    8. Zhi‐Long Chen, 1998. "Solution algorithms for the parallel replacement problem under economy of scale," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(3), pages 279-295, April.

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