[ Exercise 15 ]
- Incomplete data,
commonly referred to as censored data, often occurs when the response
variable is time to failure, e.g., accelerated life testing.
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- Their method suggested
using either iterative least squares (ILS) to analyze censored
data or the initial fit to estimate the expected failure time
for each censored observation.
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- The TOPSIS value
for each trial and the optimal factor/level combination can be
determined in the following steps:
Apply equations (4) ~ (8) to compute the relative closeness of
each trial.
Set the TOPSIS value in the ith trial to the designated value.
Estimate the factor effects based on TOPSIS value.
Determine the optimal control factors and their levels.
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- The systems manager
is in no case responsible for combining the experimental design
techniques with quality loss considerations and carefully considering
how the various factors affect performance variation.
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- Herein, TOPSIS
is applied to reduce the computational complexity, satisfy Taguchi's
quality's loss, and find a performance measurement index for each
trial.
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- The proposed procedure
is employed to transform the relative importance of each response,
compute the quality loss, determine the TOPSIS value, select the
optimal factor/level combination, and analyze a confirmation experiment.
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- The engineer adjusts
the processing parameters and finalizes the shop floor layout.
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- The proposed mechanism
is adaptive, flexible, efficient, and applicable in a factory
setting.
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- This section not
only presents a numerical example, but also demonstrates the effectiveness
of the proposed GA-based procedure for cell formation problems.
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- The censored data
contain less information than complete data and make analysis
more difficult to perform.
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- The proposed model
not only performs diagnostic checking, but also determines the
optimal factor/level combination.
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- The procedure to
determine the optimal factor/level combination in a multi-response
problem is described as follows:
Step 1: Estimate the factor effects.
A. Plot the factor effects and tabulate the main effects on
MRSN.
B. Plot the factor efforts and tabulate the main effects on
the mean response for the nominal-the-best case.
Step 2: Determine the optimal control factors and their levels.
A. Find the control factor that significantly affects MRSN.
B. Determine the optimum level for each control factor.
Step 3: Determine the optimal adjustment factors.
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