1. Chang’an University, Xi’an, China
DISCUSSION OF MULTI-OBJECTIVE OPTIMIZATION IN DECISION MAKING IN TRANSPORT INFRASTRUCTURE ASSEST MANAGEMENT
Lin CHEN
Chang’an University, Xi’an, China
July 12, 2017

2. University of Auckland
About me
Chang’an University
China
University of Auckland
New Zealand
I spent 4 years in University of Auckland to pursue my phd.
Then after the graduation, I came back to my hometown Xi’an and now I work at Chang’an university

3. University of Pretoria
About me
University of Pretoria
South Africa
This month, I’m so lucky to come to the university of pretoria for a one-month post-doc program on big data analysing and mining.

4. Content 1. Road Development in China
2. Infrastructure Asset Management
3. Decision Making
4. Multi-Objective Optimization

5. 1. Road Development in China
History:
1949:
1980s:
Management department
Laws
Lower classes of highways
Medium and high classes of highways
In 1949 when our country is established, we also start an new era on our road.
In 1980s, we noticed the importance of higher classes of highways and put our effort on it.

6. Expressway Development in China
1. Road Development in China
Just like Mr Kevin Wall told me a month ago, other countries are developing but we are running. In 25 years we built a huge number of expressway
Expressway Development in China

7. Main Road Networks in China
1. Road Development in China
4.69 million km
And now we have built a very good road network
Then what?
Main Road Networks in China

8. 2. Transport Infrastructure Asset Management
When should we apply treatments and what kind of treatment should we use?
What & When?

9. 2. Transport Infrastructure Asset Management
efficiently manage a road network in order to achieve goals and requirements
What treatments should be used?
When should I apply the treatment?
How to implement those treatment?
What are the outcomes of my management?
When should you apply treatment, what treatment should you choose, how to implement these treatments and the reason of that.

10. 3. Decision Making Decision Making (DM)
attempts to identify the proper strategies for an infrastructure asset network
Strategies
Strategy Index
Analysis period
2017
2018
2019
● ● ● 1
Treatment 1 2
Treatment 2 3 4
Treatment 3
● ● ●

11. 3. Decision Making Decision Making (DM)
attempts to identify the proper strategies for an infrastructure asset network
Each solution represent a selection of strategies

12. 3. Decision Making Decision Making (DM)
attempts to identify the proper strategies for an infrastructure asset network
Cost

13. 3. Decision Making Decision Making (DM)
attempts to identify the proper strategies for an infrastructure asset network
Condition

14. ? 3. Decision Making Decision Making (DM)
attempts to identify the proper strategies for an infrastructure asset network ?
Condition
Cost

15. Multi-Objective Optimisation
3. Decision Making
Decision Making (DM)
A wide range of considerations
A large number of segments and strategies
Conflicting goals
A number of requirements
Subjectivities
Various variables
Powerful & cheap
computing power
Multiple objectives
Multiple constraints
No expert experience needed
Multi-Objective Optimisation
(MOO)

16. 4. Multi-Objective Optimization
Multi-Objective Optimisation (MOO)
describes Multi-Objective Decision Making more practically and rational, especially when objectives cannot be reasonably aggregated into a single objective.

17. Best possible solutions
4. Multi-Objective Optimization
Multi-Objective Optimisation (MOO) - Abilities
Pareto solutions:
Best possible solutions

18. Best achievable outcomes
4. Multi-Objective Optimization
Multi-Objective Optimisation (MOO) - Abilities
Best achievable outcomes

19. 4. Multi-Objective Optimization
Multi-Objective Optimisation (MOO) - Abilities
Outcome relationship
Outcome trade-offs

20. 4. Multi-Objective Optimization
Multi-Objective Optimisation (MOO) - Abilities
Outcome relationship
Outcome trade-offs

21. 4. Multi-Objective Optimization
MOO has the potentials to help with decision making in TIAM
What are the applicable methods?
Which one is suitable for my problem?
How to implement it?
What are the applicable methods?
Which one is suitable for my problem?
How to use it?

22. 4. Multi-Objective Optimization
Weighted Sum Method (WSM)
Dichotomic Approach (DA)
Epsilon Constraint method (ECM)
Revised Normal Boundary Intersection (RNBI)
Genetic Algorithm (GA)
Nondomianted Sorting Genetic Algorithm II (NSGA II)
Simulated Annealing (SA)
Tabu Search (TS)
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Exact method
Case Study
Heuristic

23. Comparison of MOO methods
4. Multi-Objective Optimization
Comparison of MOO methods
Criterion
WSM DA
ECM
RNBI GA
NSGA II SA TS
ACO
PSO
Type
exact method
heuristic
Objectives √ 2
Constraints
type
hardness
Large size
Solutions
distribution
quality & distribution
Speed
depends
controllable & slow
Implementation
theory & math
framework

24. 4. Multi-Objective Optimization
Solution Quality

25. Solution Distribution
4. Multi-Objective Optimization
Solution Distribution
Good distribution Poor distribution

26. Comparison of MOO methods
4. Multi-Objective Optimization
Comparison of MOO methods
Criterion
WSM DA
ECM
RNBI GA
NSGA II SA TS
ACO
PSO
Type
exact method
heuristic
Objectives √ 2
Constraints
type
hardness
Large size
Solutions
distribution
quality and distribution
Speed
depends
controllable & slow
Implementation
theory & math
framework

27. Thank you!