A New Selection Procedure For Large Scale Problems
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Date
2012-04
Authors
Almomani, Mohammad Hani
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
This thesis considers the problem of selecting the stochastic system that has the best (maximum
or minimum) expected performance measure when the number of alternatives is finite but
large. Ranking and selection procedures have been used successfully for solving problems with
small number of alternatives. In order to reduce the computational problem, the idea of ordinal
optimization is being used with the objective of finding a good enough system instead of looking
for the best system. In this thesis, a new selection approach is proposed for selecting a good
system when the number of alternatives is very large. This approach contains four stages; In the
first stage, the ordinal optimization procedure is used for selecting a small subset that overlaps
with the set that contains the actual best m% systems with high probability. Then, the optimal
computing budget allocation procedure is applied in the second stage to allocate the available
computing budget. This is followed with the subset selection procedure to get a smaller subset
that contains the best system among the subset that was selected before with high probability.
Finally, indifference-zone procedure is used to select the best system from the previous subset
with high probability. The efficiency of the proposed selection approach is being examined
from two different points. First, based on some parameters changing such as the initial sample
size, increment in simulation samples, total budget, and the elapsed time. Secondly, based on
three sets of the stopping rules such as sequential, expected opportunity cost and probability of
good selection of the stopping rule. In addition, comparisons between the proposed selection
approach and the Three-stage selection approach are also presented. Finally, one of the most
difficult problem in designing of production lines, which is known as buffer allocation problem
is presented as a real application for the proposed approach. The implementations of our
approach are presented with some numerical examples. The results show that in general, the
proposed selection approach made the correct selection with high probability.
Description
Keywords
New selection approach is proposed for selecting , when the number of alternatives is very large