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Optimal Capacity Expansion Planning When There are Learning Effects

Author

Listed:
  • Randall S. Hiller

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Jeremy F. Shapiro

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

Production and capacity expansion decisions are difficult to analyze when there is learning. Later production is less costly, and maybe more profitable, but the company must endure high initial production costs. Mixed integer programming models are presented for optimizing coordinated production and capacity expansion plans in the face of such learning effects. An illustrative model is developed, optimized, and the types of strategies it selects are discussed.

Suggested Citation

  • Randall S. Hiller & Jeremy F. Shapiro, 1986. "Optimal Capacity Expansion Planning When There are Learning Effects," Management Science, INFORMS, vol. 32(9), pages 1153-1163, September.
    • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:9:p:1153-1163
    DOI: 10.1287/mnsc.32.9.1153
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    Citations

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    Cited by:

    1. Lee, Chia-Yen & Charles, Vincent, 2022. "A robust capacity expansion integrating the perspectives of marginal productivity and capacity regret," European Journal of Operational Research, Elsevier, vol. 296(2), pages 557-569.
    2. Bradley, James R., 2005. "Optimal control of a dual service rate M/M/1 production-inventory model," European Journal of Operational Research, Elsevier, vol. 161(3), pages 812-837, March.
    3. Jan A. Van Mieghem, 2003. "Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 269-302, July.
    4. Martínez-Costa, Carme & Mas-Machuca, Marta & Benedito, Ernest & Corominas, Albert, 2014. "A review of mathematical programming models for strategic capacity planning in manufacturing," International Journal of Production Economics, Elsevier, vol. 153(C), pages 66-85.
    5. Sun, Xiaojie & Tang, Wansheng & Zhang, Jianxiong & Chen, Jing, 2021. "The impact of quantity-based cost decline on supplier encroachment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    6. Ben Klemens, 2021. "Attributing Value to Patents and Trademarks in Complex Production Chains," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 842-875, June.
    7. Metin Çakanyıldırım & Robin O. Roundy & Samuel C. Wood, 2004. "Optimal machine capacity expansions with nested limitations under stochastic demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(2), pages 217-241, March.
    8. Mazzola, Joseph B. & Neebe, Alan W. & Rump, Christopher M., 1998. "Multiproduct production planning in the presence of work-force learning," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 336-356, April.
    9. Cantamessa, Marco & Valentini, Carlo, 2000. "Planning and managing manufacturing capacity when demand is subject to diffusion effects," International Journal of Production Economics, Elsevier, vol. 66(3), pages 227-240, July.
    10. Wei Zhang & Long Gao & Mohammad Zolghadr & Dawei Jian & Mohsen ElHafsi, 2023. "Dynamic incentives for sustainable contract farming," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2049-2067, July.
    11. Shahzad Bhatti & Michael Lim & Ho-Yin Mak, 2015. "Alternative fuel station location model with demand learning," Annals of Operations Research, Springer, vol. 230(1), pages 105-127, July.
    12. Stephen Shum & Shilu Tong & Tingting Xiao, 2017. "On the Impact of Uncertain Cost Reduction When Selling to Strategic Customers," Management Science, INFORMS, vol. 63(3), pages 843-860, March.

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