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Capacity Expansion and Replacement in Growing Markets with Uncertain Technological Breakthroughs

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Author Info

  • Sampath Rajagopalan

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089-1421)

  • Medini R. Singh

    (The Amos Tuck School of Business Administration, Dartmouth College, Hanover, New Hampshire 03755)

  • Thomas E. Morton

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

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    Abstract

    The accelerated pace of technological change has led to rapid obsolescence of productive capacity in electronics and other industries. Managers must consider the impact of future technologies while making acquisition and replacement decisions in such environments. We consider a problem where a sequence of technological breakthroughs are anticipated but their magnitude and timing are uncertain. A firm, operating in such an environment, must decide how much capacity of the current technology to acquire to meet future demand growth. It must also determine whether to upgrade any of the older vintages. We formulate this problem and present some structural results. Using these results, we then develop a highly efficient regeneration point-based dynamic programming algorithm. The effectiveness of the proposed algorithm is illustrated through a computational study. The sensitivity of the first period decision to various parameters is also explored.

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    File URL: http://dx.doi.org/10.1287/mnsc.44.1.12
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 1 (January)
    Pages: 12-30

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    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:1:p:12-30

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    Related research

    Keywords: Capacity Expansion; Equipment Replacement; Technology Adoption models; Stochastic Dynamic Programming; Management of Technology;

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    Cited by:
    1. Huisman, K.J.M. & Kort, P.M., 1999. "Strategic Technology Investment under Uncertainty," Discussion Paper 1999-18, Tilburg University, Center for Economic Research.
    2. Rahman, Atiqur & Loulou, Richard, 2001. "Technology acquisition with technological progress: effects of expectations, rivalry and uncertainty," European Journal of Operational Research, Elsevier, vol. 129(1), pages 159-185, February.
    3. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    4. Hadjinicola, George C. & Soteriou, Andreas C., 2003. "Reducing the cost of defects in multistage production systems: A budget allocation perspective," European Journal of Operational Research, Elsevier, vol. 145(3), pages 621-634, March.
    5. Lin, James T. & Chen, Tzu-Li & Chu, Hsiao-Ching, 2014. "A stochastic dynamic programming approach for multi-site capacity planning in TFT-LCD manufacturing under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 148(C), pages 21-36.
    6. Yatsenko, Yuri & Hritonenko, Natali, 2008. "Properties of optimal service life under technological change," International Journal of Production Economics, Elsevier, vol. 114(1), pages 230-238, July.
    7. Huisman, K.J.M. & Kort, P.M., 2003. "Strategic investment in technological innovations," Open Access publications from Tilburg University urn:nbn:nl:ui:12-90069, Tilburg University.
    8. Porteus, Evan L. & Angelus, Alexandar & Wood, Samuel C., 2000. "Optimal Sizing and Timing of Modular Capacity Expansions," Research Papers 1479r2, Stanford University, Graduate School of Business.
    9. Chand, Suresh & McClurg, Tim & Ward, Jim, 2000. "A model for parallel machine replacement with capacity expansion," European Journal of Operational Research, Elsevier, vol. 121(3), pages 519-531, March.

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