new applications for logic planning of traditional and agile projects

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.

kissjudit@gtk.uni-pannon.hu