Chao FANG Supervisors: J.C. BOCQUET, Franck MARLE Project Management Research Group
19
22/3/25 1 Analysis of Risk Network Behavior in Project Management Based on Simulation and Mathematical Modeling Chao FANG Supervisors: J.C. BOCQUET, Franck MARLE Project Management Research Group LGI, Ecole Centrale Paris
-
Upload
wilma-mcmahon -
Category
Documents
-
view
21 -
download
0
description
Analysis of Risk Network Behavior in Project Management Based on Simulation and Mathematical Modeling. Chao FANG Supervisors: J.C. BOCQUET, Franck MARLE Project Management Research Group LGI, Ecole Centrale Paris. Outline. Background Risk Interaction Network Model Simulation Analysis - PowerPoint PPT Presentation
Transcript of Chao FANG Supervisors: J.C. BOCQUET, Franck MARLE Project Management Research Group
23/4/19 1
Analysis of Risk Network Behavior in Project Management Based on Simulation and Mathematical Modeling
Chao FANG
Supervisors: J.C. BOCQUET, Franck MARLE
Project Management Research Group
LGI, Ecole Centrale Paris
23/4/19 2
Outline
Background Risk Interaction Network Model Simulation Analysis Summary & Future Work
23/4/19 3
Outline
Background Risk Interaction Network Model Simulation Analysis Summary & Future Work
23/4/19 4
Importance of Project Risk Management
Society is more than ever exposed and averse to risks
Project management is basically a risky activity: targets and constraints uncertainty and change complex with interrelated parameters
Complexity of Project and Risks
The growing complexity of project induces the complexly interacted risk network.
Current Methodologies and Problems
Most classical methods of PRM independently analyze individual risks
23/4/19 6
Current Methodologies and Problems
Tree-structure methods still focus on single risk
Bayesian Belief Network Not practical for large network Not adapt to properties of risk
interactions Could not model “loop”
23/4/19 7
23/4/19 8
Outline
Background Risk Interaction Network Model Simulation Analysis Summary & Future Work
23/4/19 9
Methodologies in Modeling Process
Identification of interactions Expertise DSM (Design Structure Matrix)
Evaluation of interactions Expertise Previous experience
Modeling of risk network By simulation By mathematical algorithm
23/4/19 10
Original Project Risk List (case study)
23/4/19 11
Risk Interaction Matrix (case study)
23/4/19 12
Risk Interaction Network (case study)
23/4/19 13
Outline
Background Risk Interaction Network Model Simulation Analysis Summary & Future Work
23/4/19 14
Simulation Model in ARENA (case study)
T r u e
F a ls e
D e c id e R 2 R 2A s s ig n R 2 S e p a r a t e R 2O r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 4 R 4A s s ig n R 4 S e p a r a t e R 4O r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 8 R 8A s s ig n R 8
T r u e
F a ls e
D e c id e R 1 0 R 1 0A s s ig n R 1 0 R 1 0S e p a r a t e
O r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 1 9 R 1 9A s s ig n R 1 9R 1 9
S e p a r a t eO r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 6 R 6A s s ig n R 6T r u e
F a ls e
D e c id e R 1 3 R 1 3A s s ig n R 1 3R 1 3
S e p a r a t eO r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 1 7 R 1 7A s s ig n R 1 7R 1 7
S e p a r a t eO r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 9 R 9A s s ig n R 9 S e p a r a t e R 9O r ig in a l
D u p lic a t e
T r u e
F a ls e
D e c id e R 3 R 3A s s ig n R 3 S e p a r a t e R 3O r ig in a l
D u p lic a t e
C r e a t eI n it ia liz a t io nA s s ig n S e p a r a t e 0
O r ig in a l
D u p lic a t e
P r o bS p o n t a n e o u s
A s s ig nR o u t e 0
S t a t io n 8
S t a t io n 2
S t a t io n 4
S t a t io n 1 9 S t a t io n 1 0
S t a t io n 1 3 S t a t io n 6
S t a t io n 1 7
S t a t io n 9
S t a t io n 3
R o u t e 2O u tA s s ig n R 2
O u tA s s ig n R 1 0R o u t e 1 0
O u tA s s ig n R 9R o u t e 9
S t a t io n 1
S t a t io n 7
S t a t io n 1 2
S t a t io n 1 5
S t a t io n 5
S t a t io n 1 6
S t a t io n 1 4
S t a t io n 1 1
D e c id e R 5T r u e
F a ls e
A s s ig n R 5 R 5
D e c id e R 1T r u e
F a ls e
A s s ig n R 1 R 1
D e c id e R 1 6T r u e
F a ls e
A s s ig n R 1 6 R 1 6
S e p a r a t e R 1O r ig in a l
D u p lic a t e
O u tA s s ig n R 1 R o u t e 5
D e c id e R 1 4T r u e
F a ls e
A s s ig n R 1 4 R 1 4
D e c id e R 1 2T r u e
F a ls e
A s s ig n R 1 2 R 1 2
D e c id e R 1 1T r u e
F a ls e
A s s ig n R 1 1 R 1 1
D e c id e R 7T r u e
F a ls e
A s s ig n R 7 R 7
D e c id e R 1 5T r u e
F a ls e
A s s ig n R 1 5 R 1 5
R 1 4S e p a r a t e
O r ig in a l
D u p lic a t e
O u tA s s ig n R 1 4R o u t e 1 4
S e p a r a t e 1 5O r ig in a l
D u p lic a t e
S t a t io n 2 0
S t a t io n 1 8
D e c id e R 2 0T r u e
F a ls e
D e c id e R 1 8T r u e
F a ls e
A s s ig n R 2 0
A s s ig n R 1 8
R 2 0
R 1 8
R 2 0S e p a r a t e
O r ig in a l
D u p lic a t e
O u tA s s ig n R 2 0
R o u t e 2 0
R o u t e 7S e p a r a t e R 7O r ig in a l
D u p lic a t e
O u tA s s ig n R 7
D is p o s e 1
0
0 0
0
0
0
0 0
0
0
0
0 0
0
0 0
0
0
0
0 0
0
0
0
0 0
0
0 0
0
0
0
0 0
0
0
0
0 0
0
0
0
0 0
0
0
0
0
0
0
0 0
0
0 0
0
0 0
0
0
0
0 0
0
0 0
0
0 0
0
0 0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23/4/19 15
Simulation Results (case study)
Some improbable risks have significant raising ratio
Some underestimated risks escalate into higher level
23/4/19 16
Outline
Background Risk Interaction Network Model Simulation Analysis Summary & Future Work
23/4/19 17
Summary
Investigation of limits in literature
Risk interaction network model
Case study on a real project
Simulation results compared with classical methods Underestimation of some risks with classical analysis due to the
false independence assumption
23/4/19 18
Future Work
Develop a mathematical model in order to approximate the behavior of the network (to be compared with simulation)
Analyze consequence of complex scenarios like “loop”
Tramway infrastructure project case study
More parameters like time and cost of risks will be included and calculated
Potential risk response and control solution
23/4/19 19
Merci de votre attention!
