Planning under uncertainty solving large-scale stochastic linear programs
- Stanford Univ., CA (United States). Dept. of Operations Research Technische Univ., Vienna (Austria). Inst. fuer Energiewirtschaft
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.
- Research Organization:
- Stanford Univ., CA (United States). Systems Optimization Lab.
- Sponsoring Organization:
- USDOE; EPRI; USDOD; National Science Foundation (NSF); AUSF; USDOE, Washington, DC (United States); Electric Power Research Inst., Palo Alto, CA (United States); Department of Defense, Washington, DC (United States); National Science Foundation, Washington, DC (United States); Fonds zur Foerderung der Wissenschaftlichen Forschung, Wien (Austria)
- DOE Contract Number:
- FG03-92ER25116
- OSTI ID:
- 6169922
- Report Number(s):
- SOL-92-8; ON: DE93013920; CNN: ECS-8906260; J0323-PHY; RP8080-09; N00014-89-J-1659
- Country of Publication:
- United States
- Language:
- English
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