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Title: Solving linear programs under uncertainty, using decomposition, importance sampling and parallel processors. Progress report

Technical Report ·
DOI:https://doi.org/10.2172/10142091· OSTI ID:10142091
; ;

Planning under uncertainty is a fundamental problem of decision science where solution could advance man`s ability to plan, schedule, design, and control complex situations. Goal is to develop efficient methods for solving an important class of planning problems, namely linear programs whose parameters (coefficients, right hand sides) are not known with certainty. The research concentrated on theoretical tasks of decomposition and importance sampling techniques, implementation, and software development issues and on applications. Research is continuing on use of parallel processors for solving stochastic programs.

Research Organization:
Stanford Univ., CA (United States). Dept. of Operations Research
Sponsoring Organization:
USDOE, Washington, DC (United States)
DOE Contract Number:
FG03-92ER25116
OSTI ID:
10142091
Report Number(s):
DOE/ER/25116-T2; ON: DE94009898; BR: KC0701010
Resource Relation:
Other Information: PBD: 1 Mar 1994
Country of Publication:
United States
Language:
English

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