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Sequential Capacity Expansion Options

Author

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  • Alain Bensoussan

    (International Center for Decision and Risk Analysis, Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080; College of Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR)

  • Benoît Chevalier-Roignant

    (King’s Business School, King’s College London, London WC2B 4BG, United Kingdom)

Abstract

This paper considers a firm’s capacity expansion decisions under uncertainty. The firm has leeway in timing investments and in choosing how much capacity to install at each investment time. We model this problem as the sequential exercising of compound capacity expansion options with embedded optimal capacity choices. We employ the impulse control methodology and obtain a quasi-variational inequality that involves two state variables: an exogenous, stochastic price process and a controlled capacity process (without a diffusion term). We provide a general verification theorem and identify—and prove the optimality of—a two-dimensional ( s , S ) -type policy for a specific (admittedly restrictive) choice of the model parameters and of the running profit. The firm delays investment in capacity to ensure that the perpetuity value of newly installed capacity exceeds the total opportunity cost, including the fixed cost component, by a sufficient margin. Our general model for “the option to expand” transcends a single-option exercise and yields predictions of both the optimal investment timing and the optimal scale of production.

Suggested Citation

  • Alain Bensoussan & Benoît Chevalier-Roignant, 2019. "Sequential Capacity Expansion Options," Operations Research, INFORMS, vol. 67(1), pages 33-57, January.
  • Handle: RePEc:inm:oropre:v:67:y:2019:i:1:p:33-57
    DOI: 10.1287/opre.2018.1752
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    References listed on IDEAS

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    Cited by:

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    2. Ebina, Takeshi & Matsushima, Noriaki & Nishide, Katsumasa, 2022. "Demand uncertainty, product differentiation, and entry timing under spatial competition," European Journal of Operational Research, Elsevier, vol. 303(1), pages 286-297.
    3. Mike Ludkovski, 2022. "Regression Monte Carlo for Impulse Control," Papers 2203.06539, arXiv.org.
    4. Perera, Sandun & Gupta, Varun & Buckley, Winston, 2020. "Management of online server congestion using optimal demand throttling," European Journal of Operational Research, Elsevier, vol. 285(1), pages 324-342.
    5. Haejun Jeon, 2023. "Time-to-build and capacity expansion," Annals of Operations Research, Springer, vol. 328(2), pages 1461-1494, September.
    6. Dabadghao, Shaunak S. & Chockalingam, Arun & Soltani, Taimaz & Fransoo, Jan C., 2021. "Valuing switching options with the moving-boundary method," Other publications TiSEM 45fe7e78-129f-4d41-ac2f-5, Tilburg University, School of Economics and Management.
    7. Briest, Gordon & Lauven, Lars-Peter & Kupfer, Stefan & Lukas, Elmar, 2022. "Leaving well-worn paths: Reversal of the investment-uncertainty relationship and flexible biogas plant operation," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1162-1176.

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