Planning under uncertainty using parallel computing. Technical report

Technical Report · Thu Jan 01 04:00:00 EST 1987

OSTI ID:5805465

Dantzig, G B

Industry and government routinely solve deterministic mathematical programs for planning and scheduling purposes, some involving thousands of variables with a linear or nonlinear objective and inequality constraints. The solutions obtained are often ignored because they don't properly hedge against future contingencies. It is relatively easy to reformulate models to include uncertainty. The bottleneck has been (and is) capability to solve them. The time is now ripe for finding a way to do so. To this end, this paper described how large-scale system methods for solving multi-staged systems, such as Bender's Decomposition, high-speed sampling or Monte Carlo simulation, and parallel processors can be combined to solve some important planning problems involving uncertainty. For example, parallel processors may make it possible to come to better grips with the fundamental problems of planning, scheduling, design, and control of complex systems such as the economy, an industrial enterprise, an energy system, a water-resource system, military models for planning and control, decisions about investment, innovation, employment, and health-delivery systems.

• Research Organization: Stanford Univ., CA (United States). Systems Optimization Lab.
• OSTI ID: 5805465
• Report Number(s): AD-A-180254/5/XAB; SOL--87-1
• Country of Publication: United States
• Language: ENGLISH

Related Subjects

99 GENERAL AND MISCELLANEOUS
990210* -- Supercomputers-- (1987-1989)
CONTROL SYSTEMS
LINEAR PROGRAMMING
MILITARY EQUIPMENT
MONTE CARLO METHOD
PARALLEL PROCESSING
PLANNING
SAMPLING
SIMULATION
STOCHASTIC PROCESSES