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5.2 Decomposition and importance sampling for stochastic linear models

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  • Entriken, Robert
  • Infanger, Gerd

Abstract

Linear models that have uncertain parameters with known probability distributions are called stochastic linear models. This paper focuses on the difficulties introduced by these stochastic parameters and reviews different approaches to handle them. The following solution method uses decomposition techniques and importance sampling, and its illustration is based upon a case study of a power system with random fluctuations in demand and equipment availabilities. Numerical results are presented.

Suggested Citation

  • Entriken, Robert & Infanger, Gerd, 1990. "5.2 Decomposition and importance sampling for stochastic linear models," Energy, Elsevier, vol. 15(7), pages 645-659.
    • Handle: RePEc:eee:energy:v:15:y:1990:i:7:p:645-659
    DOI: 10.1016/0360-5442(90)90012-Q
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    References listed on IDEAS

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    1. HO, James K. & LOUTE, Etienne, 1981. "A set of staircase linear programming test problems," LIDAM Reprints CORE 596, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    3. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
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    1. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
    2. Chen Peng & Feryal Erhun & Erik F. Hertzler & Karl G. Kempf, 2012. "Capacity Planning in the Semiconductor Industry: Dual-Mode Procurement with Options," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 170-185, April.

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