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New Applications for Logic planning of traditional and agile projects. Judit Kiss PhD candidate. Content of the presentation. Matlab applications. genetic algorithm based on GAlib. Project management approaches *. Software development, product development projects. - PowerPoint PPT Presentation
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NEW APPLICATIONS FOR LOGIC PLANNING OF TRADITIONAL AND
AGILE PROJECTS
Judit KissPhD candidate
Content of the presentation
Traditional project planning• Gantt chart & network planning methods
Agile project planning• Uncertain tasks and relations• Flexible matrix-based project planning techniques
Computer applications• PGRA• APPA• MPPGA
Simulation Results
Matlab applications
genetic algorithm based on GAlib
2/21
Project management approaches*
How?Clear Not clear
What?
Clear
Traditional(TPM) Agile (APM)
Not clear
Mertxe (MPx) Extrem (xPM)
* Wysocki, Robert K.: Effective Project Management: Traditional, Agile, Extreme, Wiley Publishing, Inc., Indianapolis, Indiana, 5th ed., 2009, ISBN 978-0-470-42367-7.
20% 70%
10%
R&D projects
Construction projects Software development, product development
projects
3/21
Project: Date: 2010,05,29
Phases / Work packages
4.1.1 4.1.3 4.1.4 4.1.5 4.2.2 4.2.3 4.2.4 4.2.5
17,12,07 18,01,08 14,01,08 13,06,08 14,01,08 13,06,08 02,06,08 13,06,08 20,12,07 28,01,08 15,01,08 28,03,08 14,01,08 13,06,08 15,01,08 15,03,08
4.3.1 4.4.1 A 4.4.6 F 4.5.1 A 4.5.6 F 4.6.1 4.7.1 4.8.1 4.9.1
- - 28,01,08 28,02,08 28,01,08 28,02,08 25,02,08 04,04,08 25,02,08 04,04,08 31,03,08 04,04,08 04,02,08 21,03,08 22,02,08 04,04,08 12,05,08 13,06,08
28,01,08 29,02,08 28,01,08 29,02,08 04,02,08 in progress 22,02,08 in progress
4.3.2 4.4.2 B 4.4.7 G 4.5.2 B 4.5.7 G 4.6.2 4.7.2 4.8.2 4.9.2
14,01,08 25,01,08 28,01,08 28,02,08 28,01,08 28,02,08 25,02,08 04,04,08 25,02,08 04,04,08 07,04,08 11,04,08 21,04,08 30,05,08 04,03,08 30,03,08 12,05,08 13,06,08
14,01,08 25,01,08 28,01,08 29,02,08 28,01,08 29,02,08
4.3.3 4.4.3 C 4.4.8 H 4.5.3 C 4.5.8 H 4.6.3 4.8.3 4.9.3
14,01,08 25,01,08 28,01,08 28,02,08 20,02,08 07,03,08 25,02,08 04,04,08 25,02,08 04,04,08 14,04,08 18,04,08 01,05,08 09,05,08 12,05,08 13,06,08
14,01,08 25,01,08 28,01,08 29,02,08 28,01,08 in progress
4.3.4 4.4.4 D 4.4.9 I 4.5.4 D 4.5.9 I 4.9.4
15,01,08 25,01,08 28,01,08 28,02,08 28,01,08 07,03,08 25,02,08 04,04,08 25,02,08 04,04,08 02,06,08 13,06,08
15,01,08 25,01,08 28,01,08 29,02,08 28,01,08 in progress
4.4.5 E 4.5.5 E 4.5.10 J
28,01,08 28,02,08 25,02,08 04,04,08 25,02,08 04,04,08
28,01,08 29,02,08 25,02,08 in progress
Milestones4.1.6
(End) User Training
Implement Finance
Implement Logistic Execution
Implement add ons & interfaces
Change Request Handling
Execute integration test
Interfacing RAMIR
Implement Sales
Prepare (End) user training
Implement Production
29,02,08
18,04,08
4.8.4
Go live completed
Design add ons and interfaces
Execute cut over & go-live
Fix bugs and retest
4.6.4
04,04,08
Project close down
Final preparation & go live
Implementation
Plan cut over
SAP Authorithy
Prepare & Test cut over
Execute end user training
Gap Analysis
Design forms
Gap analysis Sales
Gap analysis Controlling
Prepare Project Team Training
4.1.2 4.4.10 Mile Stone
Gap analyis Materials Management
Gap analysis Production
15,01,08
Gap designs approvedProject start completed
Implement FormsImplement Controlling
09,05,08
Implementation ready for I-test
Implement Material Management
4.5.11
Hand over to support organization
Project controllingProject coordination
Initialize template processes
Gap analysis Finance
SAP Basis services
Support
Prepare integration test
Integration testProject enabling
15,01,08 28,02,08
Integration test passed
Briefing local consultants
Gap analysis Logistic execution
Design RAMIR integration
13,06,08
Support end users
Complete documentation
System preparation
Work Break Down Structure
Project closed
Project start
Execute Project Team Training
Check SAP readiness of local IT infrastructure
Z…
Project Management
Plan & build local IT infrastructure updates
Complete open issues
Process of traditional project planning
4/21
Traditional vs. agile project planning
Scope Time Budget
Time Budget Scope
Fixed
Variable
Traditional project planning
Agile project planning
(Dalcher, 2009, PMUni) 5/21
Specialities of IT projects
• At logic planning prior experience can be reused
• Stochastic tasks with stochastic durations
• More possible project scenarios– Realizing tasks can be ranked by their importance– Less important tasks/functions can be left out from the project
• Stochastic relations between tasks
• More possible project structures– Tasks can be repeated or task sequences can be reversed– Flexible order of task sequences,– Several tasks can be realized parallelly and also sequentially
6/21
Matrix-based project planning methods
** Stochastic Network Planning Method (Zs.Kosztyán-J.Fejes-J.Kiss, 2008, Szigma)*** Project Expert Matrix (J. Kiss – Zs. Kosztyán, 2009, Confenis, AVA)* Dependency Structure Matrix (Steward, 1981; dsmweb.org)
A1
A2
A4
A3
SNPM - Relations between tasks can be:
0: independent/parallel relation0-1: uncertain/possible relation1: certain/sequential relation
PEM- Uncertainty of task can be:
0: task can be omitted0-1: uncertain task1: certain task
A1
A2
A4
A3
1 2 3 4
1 X X
2 X X
3 X
4
1 0,3
0,5 0,8
1
1
0,7
0,5
0,2
•DSM *•SNPM **•PEM ***
A1
A2
A4
A3
7/21
Project scenarios - Selecting the tasksB
udge
t (€)
Solutions
…
Budget
A B C D E FA 1 0.9 0.7 0.3 0 0
B 0 0.8 0.4 0.6 0.25 0
C 0 0 1 0.5 0.5 0
D 0 0 0 0.3 1 0
E 0 0 0 0 1 0,3
F 0 0 0 0 0 0
Selected tasks: A, C, E, B, D
Step 1
8/21
A
D
C
EA
C
D
E
A
B
C
D
E
Project structures – different relations
Extended Event-driven Process Chain
Critical Path Method
A B C D E A B C D
E
Precedence Diagramming Method
Graphical Evaluation and Review Technique
A B C
D
0.5
E
0.5
B C
D
E
0,5
0,5
A
…
A B C D E
A 1 0.9 0.7 0.3 0
B 0 0.8 0.4 0.6 0.25
C 0 0 1 0.5 0.5
D 0 0 0 0.3 1
E 0 0 0 0 1DB C
A
V
V
E
Step 2
Generating all possible project structures based
on the matrix values
9/21
Selecting the optimal solution R
esou
rce
Duration
B
C
DEA
A
B
C
D E A
B
C
D
E
A B C D E
A 1 0.9 0.7 0.3 0
B 0 0.8 0.4 0.6 0.25
C 0 0 1 0.5 0.5
D 0 0 0 0.3 1
E 0 0 0 0 1
Reordering the tasks
10/21
Project scenario and structure Generating & Ranking Algorithm
• Full evaluating algorithmPEM
SNPM 1.SNPM
2k.
SNPM...
Step 1
Step 2
DSM 1.1.DSM 1.2l.
DSM 2k.2l.
DSM ...DSM 1....
Matlab application by J.Kiss, based on PSSM algorithm (Kosztyán – Kiss, 2010, DSM) 11/21
Agile Project Planning Algorithm
PEM SNPM 1. DSM 1.1.
PEM T1 T2 T3 T4 T5 T6
T1 1 1
T2 0,8 0,6 0,5
T3 0,6 0,7 0,9
T4 0,5 0,4
T5 0,3 0,1
T6 0
SNPM T1 T2 T3
T1 1
T2 0,6
T3
DSM T1 T2 T3
T1 X
T2 X
T3
DSM T1 T2 T3
T1 X
T2
T3
0 1 2 3 4 5 6 7 8 9
0
1
2
3
45
week
head
T1 T2T3
Resource limit Tim
e lim
it
0 1 2 3 4 5 6 7 8 9
0
1
2
3
45
week
head
T1
T3
Resource limit Tim
e limitT2
DSM 1.2.
What? Which tasks?
How? In which order?
How long? How much?
Step 1 Step 2
Matlab application by J. Kiss, based on the APS algorithm (Kosztyán-Kiss, 2010, Vezetéstudomány) 12/21
Matrix-based Project Planning Genetic Algorithm
PEM T1 T2 T3 T4 T5 T6
T1 1 1
T2 0,8 0,6 0,5
T3 0,6 0,7 0,9
T4 0,5 0,4
T5 0,3 0,1
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 1
T3 0 0 0
T4 1 1
T5 1 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 1 0
T3 1 0 0
T4 0 0
T5 0 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 0
T3 1 0 1
T4 0 0
T5 1 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 0
T3 1 0 1
T4 0 0
T5 1 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 1 0
T3 1 0 0
T4 0 0
T5 0 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 1
T3 0 0 0
T4 1 1
T5 1 0
T6 0
Population
Population of the new generation
Crossover, mutation
Selectio
n
Selection
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 1 0
T3 1 0 0
T4 0 0
T5 0 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 1 0
T3 1 0 0
T4 0 0
T5 0 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 1
T3 0 0 0
T4 1 1
T5 1 0
T6 0
GA application by I. Borbás
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 0
T3 1 0 1
T4 0 0
T5 1 0
T6 0
DSM T1 T2 T3 T4 T5 T6
T1 1 1
T2 1 0 0
T3 1 0 1
T4 0 0
T5 1 0
T6 0
13/21
Genetic operators– Crossover #1
DSM 1 2 3 4 5
1 1 1 1
2 1
3 1 1 1
4 1
5 1
DSM 1 2 3 4 5
1 1 1 1
2 1
3 1 1
4 1 1
5 1
1
2
1 1 3
1 1 4
1 5
1 2 3 4 5 DSM
DSM 1 2 3 4 5
1 1 1 1
2 1
3
4
5
DSM 1 2 3 4 5
1 1 1 1
2 1
3
4
5
1
2
1 1 1 3
1 4
1 5
1 2 3 4 5 DSM
Genetic algorithm
14/21
Genetic operators - Crossover #2Mom
1 2 3 4 5
1 1 0 1 1 0
2 0 1 1 1 0
3 0 0 1 1 1
4 0 0 0 1 0
5 0 0 0 0 1Child #1
1 2 3 4 5
1 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1 0
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0 1 1
3 0 0 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1 0
3 0 0 1 0
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0 1 1
3 0 0 1 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1 0
3 0 0 1 0 0
4 0 0 0 1
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0 1 1
3 0 0 1 1 1
4 0 0 0 1
5 0 0 0 0 1
Child #1
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
Child #2
1 2 3 4 5
1 1 0 0 1 0
2 0 1 0 1 1
3 0 0 1 1 1
4 0 0 0 1 1
5 0 0 0 0 1
Dad
1 2 3 4 5
1 1 1 0 0 1
2 0 1 0 1 1
3 0 0 1 0 0
4 0 0 0 1 1
5 0 0 0 0 1
15/21
Genetic operators
– Negating one or more elements
– Tournament Selector
Mutation Selection
1 2 3 4 5
1 1 1 1 0 1
2 0 1 1 1 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
1 2 3 4 5
1 1 0 1 0 1
2 0 1 1 0 0
3 0 0 1 0 0
4 0 0 0 1 1
5 0 0 0 0 1
16/21
Results of the algorithms without constraints
Size of matrix (number of
tasks)
Rate of uncertain
tasks
Rate of uncertain relations
Algorithm Run time (sec)
Importance value of the
best scenarioCost of
scenario (€)Importance value of the
best structureLead time
(day)Average
resource need (person)
10 10 10PGRA 0,93 0,50 10 0,68 37 1,92APPA 0,15 0,50 10 0.68 37 1,92
MPPGA 0,01 0,50 10 0,68 37 1,92
10 10 50PGRA 8h < APPA 0,26 0,60 9 0,71 34 1,68
MPPGA 0,14 0,60 9 0,71 34 1,68
10 50 50APPA 0,02 0,88 6 0,66 13 1,92
MPPGA 0,15 0,88 6 0,66 13 1,92
50 10 10APPA 0,44 0,76 49 0,74 186 1,47
MPPGA 28,81 0,76 49 0,73 186 1,48
50 10 50APPA 0,81 0,64 47 0,72 153 1,76
MPPGA 49,43 0,64 46 0,69 165 1,73
50 50 50APPA 0,72 0,68 42 0,72 160 1,36
MPPGA 30,56 0,63 38 0,52 141 1,85
100 10 10APPA 6,56 0,73 95 0,72 296 1,75
MPPGA 194,35 0,71 96 0,53 338 1,59
200 10 10APPA 75,21 0,73 191 0,72 666 1,50
MPPGA 4252,55 0,64 192 0,63 686 1,50
17/21
Results of the algorithms with constraints
Size of matrix (number of tasks)
Rate of uncertain
tasks
Rate of uncertain relations
Algorithm Run time (sec)
Importance value of the
best scenario
Cost of scenario
(€)
Importance value of the
best structureLead time
(day)Average
resource need (person)
Cost limit
Time limit
10 10 10APPA 0,05 0,50 9 0,68 30 2,13
9 33MPPGA 0,002 0,50 9 0,68 30 2,13
10 10 50APPA 6h <
9 31MPPGA 0,07 0,60 9 0,60 18 3,17
10 50 50 MPPGA 0,15 0,76 5 0,65 13 1,46 5 13
50 10 10 MPPGA 4,85 0,52 46 0,53 164 1,63 46 167
50 10 50 MPPGA 16,94 0,56 45 0,54 141 1,78 46 150
50 50 50 MPPGA 24,45 0,54 38 0,51 113 0,80 38 144
100 10 10 MPPGA 174,15 0,47 94 0,50 274 1,85 94 280
200 10 10 MPPGA 1323,96 0,56 189 0,51 634 1,57 190 650
18/21
...
T1
T2
T3
T2
T3
T4
T1 T5
T1
T3
T4
T5
T2
0 1 2 3 4 5 6 7 8 9
0
1
2
3
45
hét
fő
T1 T2T3
Erőforráskorlát Idő
korlá
t
DSM T1 T2 T3
T1 X
T2
T30 1 2 3 4 5 6 7 8 9
0
1
2
3
45
hét
fő
T1
T3
Erőforráskorlát Időkorlát
T2
Prior project experience PEM SNPM
project scenario
DSM/network plan
project structure
Time, cost and resource planning
PEM T1 T2 T3 T4 T5 T6
T1 1 1 0 0 0 0
T2 0,8 0,6 0,5 0 0
T3 0,6 0,7 0,9 0
T4 0,4 0,4 0
T5 0,3 0,1
T6 0
SNPM T1 T2 T3 T4 T5
T1 1 0 0 0
T2 0,6 0,5 0
T3 0,7 0,9
T4 0,4
T5
SNPM T1 T2 T3 T4
T1 1 0 0
T2 0,6 0,5
T3 0,7
T4
DSM T1 T2 T3 T4 T5
T1 X
T2 X
T3 X X
T4
T5
DSM T1 T2 T3 T4 T5
T1 X
T2 X X
T3 X X
T4
T5
T2 T3
T4
T1T5
T2 T3
T4
T1T5
DSM T1 T2 T3 T4
T1 X
T2 X
T3 X
T4
T2 T3 T4T1
...
SNPM T1
T1
SNPM T1 T2 T3
T1 1 0
T2 0,6
T3
...
...DSM T1 T2 T3
T1 X
T2 X
T3
19/21
Novelty of my research
• PEM matrix – Supporting the logic planning by handling the possible task
occurrances and possible relations– The possible solutions can be generated and ranked– Logic plans can be restructured– Applyable for traditional and agile projects
• Matrix-based applications are useful and applicable at PEM matrix with higher uncertainty as well.– APPA gives the optimal solution based on the values in the
PEM.– MPPGA is practical to get a good solution taking different
constraints and multiple objective function into account.
20/21
Thank you for your kind attention.