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Planning horizon procedures for machine replacement models with several possible replacement alternatives

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  • Suresh Chand
  • Suresh Sethi

Abstract

This paper develops a forward algorithm and planning horizon procedures for an important machine replacement model where it is assumed that the technological environment is improving over time and that the machine‐in‐use can be replaced by any of the several different kinds of machines available at that time. The set of replacement alternatives may include (i) new machines with different types of technologies such as labor‐ and capital‐ intensive, (ii) used machines, (iii) repairs and/or improvements which affect the performance characteristics of the existing machine, and so forth. The forward dynamic programming algorithm in the paper can be used to solve a finite horizon problem. The planning horizon results give a procedure to identify the forecast horizon T such that the optimal replacement decision for the first machine based on the forecast of machine technology until period T remains optimal for any problem with horizon longer than T and, for that matter, for the infinite horizon problem. A flow chart and a numerical example have been included to illustrate the algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:navlog:v:29:y:1982:i:3:p:483-493
    DOI: 10.1002/nav.3800290310
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    Citations

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    Cited by:

    1. Sampath Rajagopalan & Medini R. Singh & Thomas E. Morton, 1998. "Capacity Expansion and Replacement in Growing Markets with Uncertain Technological Breakthroughs," Management Science, INFORMS, vol. 44(1), pages 12-30, January.
    2. Milind Dawande & Srinagesh Gavirneni & Sanjeewa Naranpanawe & Suresh Sethi, 2007. "Forecast Horizons for a Class of Dynamic Lot-Size Problems Under Discrete Future Demand," Operations Research, INFORMS, vol. 55(4), pages 688-702, August.
    3. 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.
    4. 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.
    5. 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.
    6. S. Bylka & S. Sethi & G. Sorger, 1992. "Minimal forecast horizons in equipment replacement models with multiple technologies and general switching costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(4), pages 487-507, June.
    7. 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.
    8. 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.
    9. James C. Bean & Jack R. Lohmann & Robert L. Smith, 1994. "Equipment replacement under technological change," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 117-128, February.

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