Results Project 3

The configuarion of the original TPG for Project 3 is:

• Number of tasks: 78

Sensitivity Analysis

The results obtained for Project 3 and the set of test are displayed in the following figures:

• Test 1 (3 teams with 3, 4 and 5 people): Figure6.9

• Test 2 (10 teams with 1 person each): Figure 6.10

• Test 3 (6 teams with 1 person each): Figure6.11

• Test 4 (3 teams with 1 person each): Figure6.12

Figure 6.9: Project 3. Normalisation Test 1 (3 teams with 3, 4 and 5 people).

Sensitivity Analysis

Figure 6.11: Project 3. Normalisation Test 3 (6 teams with 1 person each).

Figure 6.12: Project 3. Normalisation Test 4 (3 teams with 1 person each).

The results collected and represented in the Figure6.9, and Figure6.12show a pattern of behaviour which could be situated between the results of Project 1 and Project 2. There were increases in all the tests performed to a greater or lesser extent. Yet, as it happened in previous results the increments in the completion time are always consid- erably smaller than the reductions produced by breaking certain dependencies. In this particular project the increases never reach 6% of the original completion. Although, this number entails an increase of almost 1% regarding the two previous projects as the previous test it might be produced by the nature of the GA. In spite of the fact that

Sensitivity Analysis

this statement cannot be undoubtedly validated, it might be possible to considere that this effect is produced as a result of the limited capacity of the GA already mentioned in the section 6.4.1. As it is mentioned in that section, there is likely to be a problem with respect to the average produced after thirty runs of the algorithm.

The availability of resources one more time seems to be partly responsible in those increments. When the availability of the resources is the greatest, running Test 2 Figure

6.10 there is null increase for every dependency broken. And the greater number of increments corresponds to the tests with smaller availability of resources Figure6.9and Figure 6.12.

It was constructed again the table with the top 10 more sensitive dependencies in order to verify if the sensitivity of the dependencies were affected by the composition of the teams. This fact could decreases the validity of the results obtained in Project 2. Despite the results obtained and illustrated in Table6.7showed more variance in terms of which dependencies are more sensitive in every test, it still remains relative similar between the tests. For instance, the dependency between the task 5 and the task 6 remains as top 1 dependency in Test 2, Test 3, and Test 4. Another example is the dependency between the task 6 and the task 8 that appears in all the test performed.

Top 1 Top 2 Top 3 Top 4 Top 5 Top 6 Top 7 Top 8 Top 9 Top 10 Test 1 8-9 66-67 15-56 74-75 24-54 5-6 9-12 6-8 75-76 17-56 Test 2 5-6 3-5 2-3 1-2 6-8 8-9 65-71 9-65

Test 3 5-6 6-7 73-74 3-5 59-60 8-9 6-8 48-66 34-35 36-37 Test 4 5-6 6-8 8-65 8-9 6-7 74-75 75-76 7-10 10-12 24-28

Table 6.7: Top 10 dependencies Project 3. The content represents the indexes between

the two tasks which compose the dependency. Test 1 resource composition: 3 teams (3,4,5 people). Test 2 resource composition: 10 teams (1 person). Test 3 resource composition: 6 teams (1 person). Test 4 resource composition: 3 teams (1 person).

The second test only displays 8 dependencies because there was not found other de- pendencies able to reduce the completion time. Therefore, the variability in the third factor that defines the model could have a greater importance over the sensitivity of the dependencies than it was mentioned in the results remarked in Project 2, section

6.4.2. Yet, this fact does not entail that it is not possible to present a list of sensitive dependencies that by removing could produce a considerable reduction in the overall completion time of the project. Moreover, it would be possible to feed the model with the desired configuration for a particular project to evaluate the dependencies and their impact.

In this project the decrease and increase indicated in Figure6.9and Figure6.12for Test 1 and Test 4 seem to show almost identical results regarding the original overall completion time of the project establish as 100% benchmark. Hence, it can be understood that

Sensitivity Analysis

under specific circumstances the resources are always busy and removing one particular dependency make no difference at all. Yet, this phenomenon does not involve that the impact of that particular dependency could not lead to a significant reduction in the completion time with other resource configuration. In other words, the completion time of a project is limited by the resources available but the importance in terms of sensitivity of one or more dependencies could be used in the adequate scenarios. The reduction in the completion time achieved in Test 2 and Test 3 depicted in Fig- ure 6.10 and Figure 6.11 for the first dependency broken obtained approximately 31% and 20%. It can be appreciated that the same dependency produced this effect if this information is combined with the one provided in Table 6.7. In the case of the Test 4 illustrated in Figure 6.12 the percentage of decrease for this particular dependency exceed 4%. Nevertheless, the same graph displays a considerable number of increments in the completion time.

In conclusion, even though the results analysed do not show as strong evidences as the ones evaluated in the section 6.4.2 of this paper for Project 2, it is still possible to recognise certain similarities in the patterns of behaviour. Moreover, when the data do not corroborate thoroughly the impact of the most sensitive dependencies there still is enough reasonable proof to justify the conduct of the response of the model. Thus, it rational and sensible to believe that the implementation developed is reviling interesting results in the direction of the focus of this research.