Buffer Capacity
TR and ABL results
To show the global effect of each pattern on TR and simplify the analysis, average TR results for experiments with different were calculated. For instance, results for a balanced were calculated as the average TR of experiments with patterns , and . Furthermore, as TR results have very different magnitudes for different and values, Fig. 2 shows "normalised" TR results (nTR), which were calculated by dividing the average TR results of each by the average overall TR value of all experiments with the same and values. It is worth noting that results regarding are not shown as they were equivalent. Full TR results with their corresponding Dunnett's and Tukey's tests results can be found in the "A ppendix", Tables 4 and 5 , respectively.
Fig. 2 nTR results for with , and
Results shown in Fig. 2 suggest that the performance of buffer
allocation patterns is highly dependent on the values of and . For example, the ascending (/)
performed very well with , which was only
outperformed by the balanced pattern when and for lines with . Figure 2 also suggests that the
balanced pattern performs better with increasing values of and , while the ascending pattern performs
better with decreasing values of and , and
increasing values of . These results are also confirmed by
Tables 4 and 5, as experiments with the pattern (/ , /) were found
to have statistically significant differences with the control (balanced
pattern) only when . Thus, increasing values of
resulted in lesser relative differences among the
patterns, suggesting that the effect of buffer allocation patterns on TR
is highly dependent on the reliability of the machines.
On the other hand, the descending ( \ ) was the worst pattern in terms of TR for all scenarios, a result confrmed by Tables 4 and 5 as experiments with the pattern (\ , \) had the lowest TR in all experimental conditions. Interestingly, the bowl () and inverted bowl patterns () changed their relative performance with increasing values, since the () pattern was almost on par with the good performance of () when for some scenarios; whereas the () pattern seemed to have an overall good performance for scenarios with and .
Similar to Fig. 2, Fig 3 shows the normalised ABL results () showing the relative performance in each experimental scenario (different values of and ) per .
Figure 3 suggests that ABL results are more consistent than TR results in terms of performance, since the ascending pattern is always the best-performing (with lower ABL) and the descending pattern is always the worst. Tables 6 and 7 in the "Appendix" confirm this conclusion by showing that the best pattern in terms of ABL for all scenarios is (/,/), while the worst pattern for almost all scenarios is (\,\). Contrary to the interaction effect between and on TR, higher values of resulted in a higher influence of on ABL, especially with lower values of .
The Analysis of Variance test (Table 2) shows that both reliability-related factors ( and ) have the highest influence on , followed by and the interaction between and . As seen in Figs. 2 and 3, the interactions between and , , and are also significant, albeit they have a lower effect on when compared to single factors. For ABL, is the most important factor, followed by and the interaction between and . Therefore, the performance of ABL is more dependent on selecting a good (bad) pattern.
Table 2 Analysis of variance test for TR and ABL
Factor | TR | Factor | ABL | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Df | Sum Sq | Mean Sq | F value | Pr(> F) | Df | Sum Sq | Mean Sq | F value | Pr(> F) | ||
ε | 1 | 17771 | 17771 | 4.43E+07 | 0 | BC | 1 | 494955 | 494955 | 5.72E+07 | 0 |
α | 1 | 1567 | 1567 | 3.91E+06 | 0 | ε | 1 | 3313 | 3313 | 3.83E+05 | 0 |
BC | 1 | 425 | 425 | 1.06E+06 | 0 | α | 1 | 2031 | 2031 | 2.35E+05 | 0 |
ε:α | 1 | 231 | 231 | 5.75E+05 | 0 | PP | 24 | 19032 | 793 | 9.16E+04 | 0 |
N | 1 | 155 | 155 | 3.86E+05 | 0 | BC:ε | 1 | 750 | 750 | 8.66E+04 | 0 |
BC:ε:α | 1 | 25 | 25 | 6.27E+04 | 0 | BC:α | 1 | 544 | 544 | 6.28E+04 | 0 |
N:α | 1 | 20 | 20 | 4.93E+04 | 0 | PP:BC | 24 | 6037 | 252 | 2.91E+04 | 0 |
BC:ε | 1 | 11 | 11 | 2.80E+04 | 0 | N:ε | 1 | 90 | 90 | 1.04E+04 | 0 |
N:ε:α | 1 | 6 | 6 | 1.60E+04 | 0 | N | 1 | 44 | 44 | 5.10E+03 | 0 |
BC:N | 1 | 2 | 2 | 3.82E+03 | 0 | PP:N | 24 | 1008 | 42 | 4.85E+03 | 0 |
PP | 24 | 14 | 1 | 1.47E+03 | 0 | BC:N | 1 | 35 | 35 | 4.03E+03 | 0 |
BC:α | 1 | 1 | 1 | 2.99E+03 | 0 | ε:α | 1 | 28 | 28 | 3.20E+03 | 0 |
PP:BC | 24 | 1 | 0 | 5.35E+01 | 0 | BC:N:ε | 1 | 22 | 22 | 2.53E+03 | 0 |
PP:N | 24 | 1 | 0 | 1.02E+02 | 0 | PP:α | 24 | 484 | 20 | 2.33E+03 | 0 |
PP:ε | 24 | 1 | 0 | 9.49E+01 | 0 | PP:ε | 24 | 460 | 19 | 2.22E+03 | 0 |
N:ε | 1 | 0 | 0 | 2.14E+02 | 0 | N:ε:α | 1 | 15 | 15 | 1.68E+03 | 0 |
PP:α | 24 | 1 | 0 | 7.25E+01 | 0 | PP:BC:N | 24 | 346 | 14 | 1.67E+03 | 0 |
PP:BC:N | 24 | 0 | 0 | 9.89E+00 | 0 | PP:BC:α | 24 | 135 | 6 | 6.50E+02 | 0 |
PP:BC:ε | 24 | 0 | 0 | 1.09E+01 | 0 | BC:N:α | 1 | 5 | 5 | 5.66E+02 | 0 |
PP:N:ε | 24 | 0 | 0 | 2.91E+00 | 2.37E−04 | PP:BC:ε | 24 | 104 | 4 | 5.00E+02 | 0 |
BC:N:ε | 1 | 0 | 0 | 7.59E+02 | 0 | N:α | 1 | 3 | 3 | 3.00E+02 | 0 |
PP:BC:α | 24 | 0 | 0 | 9.60E+00 | 0 | PP:ε:α | 24 | 83 | 3 | 4.02E+02 | 0 |
PP:N:α | 24 | 0 | 0 | 9.08E+00 | 0 | BC:N:ε:α | 1 | 2 | 2 | 2.18E+02 | 0 |
BC:N:α | 1 | 0 | 0 | 8.62E+02 | 0 | PP:N:ε | 24 | 21 | 1 | 1.02E+02 | 0 |
PP:ε:α | 24 | 1 | 0 | 1.09E+02 | 0 | PP:N:α | 24 | 13 | 1 | 6.11E+01 | 0 |
PP:BC:N:ε | 24 | 0 | 0 | 3.43E−01 | 0.999 | BC:ε:α | 1 | 1 | 1 | 9.43E+01 | 0 |
PP:BC:N:α | 24 | 0 | 0 | 1.10E+00 | 0.337 | PP:BC:ε:α | 24 | 25 | 1 | 1.22E+02 | 0 |
PP:BC:ε:α | 24 | 0 | 0 | 3.26E+01 | 0 | PP:N:ε:α | 24 | 20 | 1 | 9.75E+01 | 0 |
PP:N:ε:α | 24 | 0 | 0 | 1.20E+01 | 0 | PP:BC:N:ε | 24 | 10 | 0 | 4.95E+01 | 0 |
BC:N:ε:α | 1 | 0 | 0 | 8.91E+01 | 0 | PP:BC:N:α | 24 | 5 | 0 | 2.46E+01 | 0 |
PP:BC:N:ε:α | 24 | 0 | 0 | 3.76E+00 | 1.33E−07 | PP:BC:N:ε:α | 24 | 11 | 0 | 5.39E+01 | 0 |
Residuals | 314600 | 126 | 0 | Residuals | 314600 | 2724 | 0 |