00138 b34bfe1c47dc055c8d02cda05623df
139
Optimization and Sensitivity Analysis
conclusions conceming highly significant variables with respect to expected cost per time unit by changing and not changing the highly significant variables and noting the effects on expected cost.
A major obstacle to industrial implementation of the LV model is the large number of terms and difficulties in their estimation. Our results indicate that one could use as few as four input variables and observe relatively smali changes in the cost response relative to the fuli model. This study provides a basis for the investigation of the use of cost ratios rather than actual cost as a further aid to implementation.
Table 8. Results of Mis-specification Test
Inputyariables Results
X T0 T. T, E Cq C, W a b Y A Cost % change
($) {$) ($) ($) ($) ($) ($) over base
expected + ---++ + + + + + -
relalion
with cost
baseline 007 0.6 0.3 0.2 0.1 10 50 10 0.3 0.1 20 0.75 16.07 modify
non-
significant
variables
maximize 0.07 0.54 0.27 0.18 0.11 10 50 11 0.33 0.11 22 0.75 1 16.62
cost
minimize 0.07 0.66 0.33 0.22 0.09 10 50 9 0.27 0.09 18 0.75 15.52 7%
cost modify
significant
variables
maximize 0.07 0.6 0.3 0.2 0.1 11 55 10 0.3 0.1 20 0.68 18.10
cost
minimize 0.06 cost
0.6 0.3 0.2 0.1 9 45 10 0.3 0.1 20 0.83
14.20 24%
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