Research on Simulation-based Ordinal Optimization Methods with Applications to Production Scheduling of 300mm Foundry Fabs (1/3)
Date Issued
2004-07-31
Date
2004-07-31
Author(s)
DOI
922213E002099
Abstract
In this proposed three-year research
project, objectives are (1) to design search
space reduction method for simulationbased
OO, (2) to develop these methods into
tool modules as part of an integrated system
optimization platform, and (3) to apply
simulation-based OO to effective production
scheduling of 300mm foundry fabs. In the
first year, we have designed an OO-Based
Policy Iteration (OOBPI) method to handle
the combinatorial complexity of decisions
over the time axis for Stationary Markov
decision problems. Utilizing the framework
of policy iteration, we approximate the
optimal cost-to-go and optimal decision of
each state by simulation-based OO. The
OOBPI method demonstrates, in preliminary
numerical studies, two orders of speed-up in
than policy iteration using traditional
simulation for evaluating cost-to-go values.
To efficiently handle the large state space
under computing processor capacity and run
time limits, we have been investigating the
notion of contract algorithms in general and
ordinal computing budget allocation in
specific to further speed up the convergence
of OOBPI.
project, objectives are (1) to design search
space reduction method for simulationbased
OO, (2) to develop these methods into
tool modules as part of an integrated system
optimization platform, and (3) to apply
simulation-based OO to effective production
scheduling of 300mm foundry fabs. In the
first year, we have designed an OO-Based
Policy Iteration (OOBPI) method to handle
the combinatorial complexity of decisions
over the time axis for Stationary Markov
decision problems. Utilizing the framework
of policy iteration, we approximate the
optimal cost-to-go and optimal decision of
each state by simulation-based OO. The
OOBPI method demonstrates, in preliminary
numerical studies, two orders of speed-up in
than policy iteration using traditional
simulation for evaluating cost-to-go values.
To efficiently handle the large state space
under computing processor capacity and run
time limits, we have been investigating the
notion of contract algorithms in general and
ordinal computing budget allocation in
specific to further speed up the convergence
of OOBPI.
Subjects
ordinal optimization
policy
iteration
iteration
simulation-based policy iteration
Markov decision process
contract algorithm
computing budget allocation
Publisher
臺北市:國立臺灣大學電機工程學系暨研究所
Type
report
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