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Optimizing Resource Acquisition Decisions by Stochastic Programming

Author

Listed:
  • Daniel Bienstock

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

  • Jeremy F. Shapiro

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

Abstract

This paper reports on the application of stochastic programming with recourse to strategic planning decisions regarding resource acquisition. A resource directed decomposition method, which simultaneously exploits stochastic programming and mixed integer programming model structures, is proposed. Computational experience with the method applied to fuel contract and plant construction decisions faced by an electric utility is presented.

Suggested Citation

  • Daniel Bienstock & Jeremy F. Shapiro, 1988. "Optimizing Resource Acquisition Decisions by Stochastic Programming," Management Science, INFORMS, vol. 34(2), pages 215-229, February.
    • Handle: RePEc:inm:ormnsc:v:34:y:1988:i:2:p:215-229
    DOI: 10.1287/mnsc.34.2.215
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    Citations

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    Cited by:

    1. Logan, Douglas M., 1990. "5.4. Decision analysis in engineering-economic modeling," Energy, Elsevier, vol. 15(7), pages 677-696.
    2. Claudia Sagastizábal & Mikhail Solodov, 2012. "Solving generation expansion planning problems with environmental constraints by a bundle method," Computational Management Science, Springer, vol. 9(2), pages 163-182, May.
    3. Vladimirou, Hercules, 1998. "Computational assessment of distributed decomposition methods for stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 108(3), pages 653-670, August.
    4. Kim, Dowon & Ryu, Heelang & Lee, Jiwoong & Kim, Kyoung-Kuk, 2022. "Balancing risk: Generation expansion planning under climate mitigation scenarios," European Journal of Operational Research, Elsevier, vol. 297(2), pages 665-679.
    5. Benjamin F. Hobbs & Yuandong Ji, 1999. "Stochastic Programming-Based Bounding of Expected Production Costs for Multiarea Electric Power System," Operations Research, INFORMS, vol. 47(6), pages 836-848, December.
    6. 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.
    7. P. Baricelli & C. Lucas & E. Messina & G. Mitra, 1996. "A model for strategic planning under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 4(2), pages 361-384, December.
    8. Gyana R. Parija & Shabbir Ahmed & Alan J. King, 2004. "On Bridging the Gap Between Stochastic Integer Programming and MIP Solver Technologies," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 73-83, February.
    9. C A Poojari & C Lucas & G Mitra, 2008. "Robust solutions and risk measures for a supply chain planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(1), pages 2-12, January.
    10. Suleyman Karabuk & S. David Wu, 2003. "Coordinating Strategic Capacity Planning in the Semiconductor Industry," Operations Research, INFORMS, vol. 51(6), pages 839-849, December.
    11. Guldmann, Jean-Michel & Wang, Fahui, 1999. "Optimizing the natural gas supply mix of local distribution utilities," European Journal of Operational Research, Elsevier, vol. 112(3), pages 598-612, February.
    12. Jikai Zou & Shabbir Ahmed & Xu Andy Sun, 2018. "Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 388-401, May.
    13. Shapiro, Jeremy F., 1939-, 1998. "On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource based view of the firm," Working papers WP 4018-98., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    14. Shapiro, Jeremy F., 1999. "On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource-based view of the firm," European Journal of Operational Research, Elsevier, vol. 118(2), pages 295-314, October.

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