NEW APPLICATIONS FOR LOGIC PLANNING
OF TRADITIONAL AND AGILE PROJECTS
Judit Kiss
PhD 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
Matlab applications
genetic algorithm based on GAlib
Simulation Results
2/21
Project management approaches*
20%
70%
10%
* Wysocki, Robert K.: Effective Project Management: Traditional, Agile, Extreme,
Wiley Publishing, Inc., Indianapolis, Indiana, 5th ed., 2009, ISBN 978-0-470-42367-7.
3/21
Process of traditional project planning
Work Break Down Structure
Project:
Z…
Date:
2010,05,29
Phases / Work packages
Project Management
4.1.1
4.1.3
Project start
17,12,07
18,01,08
Project coordination
14,01,08
Project
enabling
4.3.1
-
-
4.3.2
Briefing local
consultants
13,06,08
4.1.5
Project controlling
14,01,08
13,06,08
A
Gap analysis Finance
F
Gap analysis
Production
28,02,08
28,01,08
28,02,08
28,01,08
29,02,08
28,01,08
29,02,08
4.4.2
B
4.4.7
G
Design forms
14,01,08
25,01,08
28,01,08
28,02,08
28,01,08
28,02,08
14,01,08
25,01,08
28,01,08
29,02,08
28,01,08
29,02,08
4.4.3
C
4.4.8
H
4.3.3
Prepare Project Team
Training
Gap analysis Sales
Design add ons and
interfaces
14,01,08
25,01,08
28,01,08
28,02,08
20,02,08
07,03,08
14,01,08
25,01,08
28,01,08
29,02,08
28,01,08
in progress
4.4.4
D
4.4.9
I
4.3.4
Execute Project Team
Training
02,06,08
Gap analysis Logistic
execution
Design RAMIR
integration
15,01,08
25,01,08
28,01,08
28,02,08
28,01,08
07,03,08
15,01,08
25,01,08
28,01,08
29,02,08
28,01,08
in progress
4.4.5
E
Gap analyis Materials
Management
28,01,08
28,02,08
28,01,08
29,02,08
13,06,08
4.2.3
Check SAP readiness of
local IT infrastructure
20,12,07
28,01,08
Implementation
4.4.6
28,01,08
Gap analysis
Controlling
4.2.2
Project close down
Gap Analysis
4.4.1
Initialize template
processes
System preparation
4.1.4
4.5.1
A
4.2.4
Plan & build local IT
infrastructure updates
15,01,08
28,03,08
Integration test
4.5.6
F
4.6.1
4.2.5
SAP Basis services
14,01,08
13,06,08
(End) User Training
4.7.1
SAP Authorithy
15,01,08
15,03,08
Final preparation &
go live
4.8.1
Prepare integration
test
Prepare (End) user
training
25,02,08
04,04,08
25,02,08
04,04,08
31,03,08
04,02,08
21,03,08
22,02,08
04,04,08
04,02,08
in progress
22,02,08
in progress
4.5.2
B
4.5.7
G
4.6.2
Implement Finance
Implement Controlling
Implement Production
Implement Forms
4.7.2
Execute integration
test
25,02,08
04,04,08
25,02,08
04,04,08
07,04,08
4.5.3
C
4.5.8
H
4.6.3
Implement Sales
Implement add ons &
interfaces
25,02,08
04,04,08
25,02,08
04,04,08
4.5.4
D
4.5.9
I
Implement Logistic
Execution
04,04,08
25,02,08
04,04,08
4.5.5
E
4.5.10
J
Implement Material
Management
04,04,08
11,04,08
4.8.2
Execute end user
training
21,04,08
Plan cut over
30,05,08
Prepare & Test cut
over
04,03,08
30,03,08
4.8.3
Fix bugs and retest
14,04,08
18,04,08
Complete open issues
12,05,08
13,06,08
4.9.2
Support end users
12,05,08
13,06,08
4.9.3
Execute cut over & golive
01,05,08
09,05,08
Complete documentation
12,05,08
13,06,08
4.9.4
Hand over to support
organization
Interfacing RAMIR
25,02,08
25,02,08
04,04,08
Support
4.9.1
02,06,08
13,06,08
Change Request
Handling
25,02,08
04,04,08
25,02,08
in progress
Milestones
4.1.2
4.4.10 Mile Stone
4.5.11
4.6.4
4.8.4
4.1.6
Project start completed
Gap designs approved
Implementation ready
for I-test
Integration test passed
Go live completed
Project closed
15,01,08
28,02,08
04,04,08
18,04,08
09,05,08
13,06,08
15,01,08
29,02,08
4/21
Traditional vs. agile project planning
Traditional project
planning
Scope
Time
Agile project
planning
Fixed
Budget
Time
Variable
(Dalcher, 2009, PMUni)
Budget
Scope
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
SNPM
PEM
1
1
2
3
4
1
2
3
4
X1 0,3
X
X 0,8
X
0,7 0,5
0,5 X1
0,2
- Uncertainty
Relations between
of task tasks
can be:
can be:
0: task
independent/parallel
can be omitted relation
0-1: uncertain
uncertain/possible
task
relation
1: certain
certain/sequential
task
relation
A3
•DSM *
•SNPM **
•PEM ***
A1
A4
A2
***
* Dependency
Project Expert
Structure
Matrix
Matrix
(J. KissMethod
(Steward,
– Zs. Kosztyán,
1981; 2009,
dsmweb.org)
Confenis, AVA)
**
Stochastic
Network
Planning
(Zs.Kosztyán-J.Fejes-J.Kiss,
2008, Szigma)
7/21
Project scenarios - Selecting the tasks
A B C D E F
A 1 0.9 0.7 0.3 0
Step 1
0.2
Budget (€)
B 0 0.8 0.4 0.6 5
C 0 0 1 0.5 0.5
D 0 0 0 0.3 1
E 0 0 0 0 1
F 0 0 0 0 0
0
0
0
0
0,3
0
Budget
…
Solutions
Selected tasks: A, C, E, B, D
8/21
Project structures – different relations
Critical Path Method
Step 2
A
A
B
B
C
C
D
D
Precedence Diagramming Method
Generating all possible
project structures based
on the matrix values
CC
AA
A
E
E
A
B
C
D
E
1
0.9
0.7
0.3
0
EE
DD
Graphical Evaluation and Review Technique
0,5
D
D
E
E
0.5
B
0
0.8
0.4
0.6
AA
0.25
B
B
CC
0.5
0,5
0
1
0.5
0.5
Extended Event-driven Process Chain
D
0
0
0
0.3
1
E
0
0
0
0
1
A
B
B
A
…
C
C
D
E
V
0
V
C
E
D
9/21
Selecting the optimal solution
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
B
B
A
D
A
E
C
E
C
Resource
D
Reordering
the tasks
C
A
E
B
D
Duration
10/21
Project scenario and structure
Generating & Ranking Algorithm
• Full evaluating algorithm
PEM
SNPM
...
SNPM 1.
Step 1
SNPM
2k.
Step 2
DSM 1.1.
DSM 1....
DSM
1.2l.
...
DSM
DSM
2k.2l.
Matlab application by J.Kiss, based on PSSM algorithm
(Kosztyán – Kiss, 2010, DSM)
11/21
Agile Project Planning Algorithm
Step 1
PEM
What?
Which tasks?
SNPM 1.
Step 2
How?
In which order?
DSM 1.1.
T1
T1
T1
T2
T1
1
1
T2
T3
T4
T5
T4
T5
T6
X
T2
SNPM T1
T2
T3
X
T3
T3
T1
0,6
0,5
0,6
0,7
0,9
0,5
0,4
0,3
T2
T3
T1
T2
T3
0 1 2 3 4 5 6 7 8 9 week
1
0,6
DSM 1.2.
0,1
0
Resource limit
DSM
T1
T2
T3
T1
T2
X
T3
head
5
Resource limit
4
3
T3
2
T2
1
T1
0
0 1 2 3 4 5 6 7 8 9 week
Matlab application by J. Kiss, based on the APS algorithm
(Kosztyán-Kiss, 2010, Vezetéstudomány)
Time limit
T6
0,8
T3
T2
head
5
4
3
2
1
0
Time limit
DSM
PEM
How long?
How much?
12/21
Matrix-based Project Planning Genetic Algorithm
Population
PEM
T1
T2
T1
1
1
T3
T4
T5
T6
DSM
T1
T2
T1
1
1
1
T2
T2
0,8
0,6
DSM
T1
T2
T1
1
1
0,5
T3
T3
T3
0,6
0,7
0,9
T5
0
DSM
0,5
T6
0,4
0,3
0,1
1
0
0
0
0
T3
T1
T2
T3
0
1
0
0
0
1
1
T2
T1
T3
T2
1
T1
T2
1
T4
0
T5
T3
T4
0
0
1
0
1
0
0
0
1
T3
T4
T6
T6
1
1
0
T1
T2
T1
1
1
1
T2
DSM
T1
T4T2
T1
1
T51
T2
T4T5
T5T6
T61
T6
T3
T4
0
1
0
T3
0
T3
T4
T5
0
T5
T6
0
1T6
1
T6
T5
T6
DSMDSM
T1
T1T2
T2T3
T11
11
1
T1
0
0
T2 T2
1
1
0
T3 T3
0
0
T5
DSM
0
T6
0
1
DSM
T4
0
T6
T5
T5
T5
T5
T6
1
T4
T1
T4
T5
T4
T4
1
T2
T4
T3
T4
0
1
T4
T5
T5
T6
T6
T6
1
T3T4
1
1
1
T3
T4
0
1 10
T5
01
1
0
0 0T6
0
0
0
0
0
0
0
1 10
0
0
0
00
10
0
0
0
0
0 0
Population
of the new
generation
DSM
T1
T2
T1
1
1
T2
T3
T4
1
T3
1
1
T4
T5
0
DSM T1
0
0
T1
1
0
0
T2
T5
T3
T6
T4
T5
GA application by I. Borbás
T6
T6
0
T2
T3
T4
DSM
T1
T2
T1
1
1
T2
T5
T6
T3
1
1
0
0
0
0
0
0
0
T4
0
1
0
0
1
T4
1
1
0
1
T3
T5
Selection
0
1
1
T5
T6
0
0
T6
0
0
13/21
Genetic operators– Crossover #1
Genetic algorithm
DSM
1
2
1
1
1
2
3
4
DSM
1
2
3
1
1
1
1
1
2
2
3
3
11
4
4
1
5
5
5
DSM
5
11
1
1
3
1
1
4
5
1
2
3
4
4
5
1
2
1
1
1
1
1
1
2
3
4
3
4
1
5
5
DSM
14/21
Genetic operators - Crossover #2
Dad
Mom
1
2
3
4
5
1
1
1
0
0
1
0
2
0
1
0
1
1
1
1
3
0
0
1
0
0
0
1
0
4
0
0
0
1
1
0
0
1
Child #2
5
0
0
0
0
1
1
2
3
4
5
1
1
0
1
1
0
2
0
1
1
1
3
0
0
1
4
0
0
5
0
0
Child #1
1
2
3
4
5
1
1
0
0
1
0
0
2
0
1
0
1
1
0
0
3
0
0
1
1
1
0
1
0
4
0
0
0
1
1
0
0
1
5
0
0
0
0
1
1
2
3
4
5
1
1
1
1
0
1
2
0
1
1
1
3
0
0
1
4
0
0
5
0
0
15/21
Genetic operators
Mutation
Selection
– Negating one or more
elements
1
2
3
4
5
1
1
01
1
0
1
2
0
1
1
01
0
3
0
0
1
0
0
4
0
0
0
1
10
5
0
0
0
0
1
– Tournament Selector
16/21
Results of the algorithms without constraints
Size of matrix
(number of
tasks)
Rate of
uncertain
tasks
Rate of
uncertain
relations
10
10
10
10
10
50
10
50
50
50
10
10
50
10
50
50
50
50
100
10
10
200
10
10
Algorithm
PGRA
APPA
MPPGA
PGRA
APPA
MPPGA
APPA
MPPGA
APPA
MPPGA
APPA
MPPGA
APPA
MPPGA
APPA
MPPGA
APPA
MPPGA
Run time
(sec)
0,93
0,15
0,01
8h <
0,26
0,14
0,02
0,15
0,44
28,81
0,81
49,43
0,72
30,56
6,56
194,35
75,21
4252,55
Importance
Importance
Cost of
Lead time
value of the
value of the best
scenario (€)
(day)
best scenario
structure
Average
resource need
(person)
0,50
0,50
0,50
10
10
10
0,68
0.68
0,68
37
37
37
1,92
1,92
1,92
0,60
0,60
0,88
0,88
0,76
0,76
0,64
0,64
0,68
0,63
0,73
0,71
0,73
0,64
9
9
6
6
49
49
47
46
42
38
95
96
191
192
0,71
0,71
0,66
0,66
0,74
0,73
0,72
0,69
0,72
0,52
0,72
0,53
0,72
0,63
34
34
13
13
186
186
153
165
160
141
296
338
666
686
1,68
1,68
1,92
1,92
1,47
1,48
1,76
1,73
1,36
1,85
1,75
1,59
1,50
1,50
17/21
Results of the algorithms with constraints
Rate of
uncertain
(number of tasks)
tasks
Size of matrix
10
10
Rate of
uncertain
relations
10
10
10
50
10
50
50
Algorithm
Importance
Cost of
Importance
Average
Run time
Lead time
Cost Time
value of the scenario value of the
resource need
(sec)
(day)
limit limit
best scenario
(€)
best structure
(person)
APPA
0,05
0,50
9
0,68
30
2,13
MPPGA
0,002
0,50
9
0,68
30
2,13
APPA
6h <
9
33
9
31
13
MPPGA
0,07
0,60
9
0,60
18
3,17
50
MPPGA
0,15
0,76
5
0,65
13
1,46
5
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
DSM
SNPM T1
T2
T3
T4
T5
1
0
0
0
T1
T1
T2
T2
T1
T2
T
1
T
2
0,6
T3
T4
T5
X
X
T4
T5
X
X
T3
T4
0,5
0
0,7
0,9
T5
T
3
T3
T4
0,4
T5
DSM
T1
T1
T2
T1
T3
T2
T4
X
T2
T5
T3
X
X
T3
X
X
T5
T4
T1
T3
T2
T5
T3
SNPM T1
T2
T1
T5
T4
PEM
T1
T2
T3
T4
T5
T6
T1
1
1
0
0
0
0
T2
T3
T3
T1
T4
T2
T5
T4
T5
T3
T4
1
0
0
DSM
T1
T1
T2
0,6
T4
...
T2
0,7
X
T3
T1
X
0,6
0,5
0
0
0,6
0,7
0,9
0
0,4
0,4
0
0,3
0,1
DSM
0
T1
...
...
T2
T3
T1
T2
T3
X
T2
X
T3
...
T1
1
T1
T2
T3
Erőforráskorlát
T1
T2
T3
T1
T2
X
T3
fő
5
4
3
2
1
0
Erőforráskorlát
T3
T1
T2
Időkorlát
T3
0,6
fő
5
4
3
2
1
0
0 1 2 3 4 5 6 7 8 9 hét
0
DSM
T2
T4
T3
T2
T4
T4
SNPM T1
T4
0,5
T2
T3
T3
X
Időkorlát
T6
0,8
T1
T2
0 1 2 3 4 5 6 7 8 9 hét
SNPM T1
T1
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.
kissjudit@gtk.uni-pannon.hu