LCC models in Banedanmark
The Danish Rail Conference 2015
Session: Research and Innovation
05-05-2015
Presented by Dorte-Lene Bacher, head of BETA, Banedanmark
1
Agenda
Developing LCC models
Methodology and structure of the models
Using the models and next steps
2
Developing LCC models
3
Why the need for LCC?
A strategic ambition and strategic objectives to ensure the ambition
A more cost effective railway
LCC
approach and
models
4
The journey from 2001 to 2015
TAM: Track Analysis Model
BAM: Bridge Analysis Model
CAM: Catenary Analysis Model
2014
2012
2011
BAM 1
CAM 0.1
2015:
TAM 2
BAM 1
CAM 0.1
TAM 2
2005-2006
TAM 1.1
2002-2003
TAM 1.0
Ongoing development and updating of
Asset Management System
5
The process
Specification of the model. Banedanmark, Civity and
engineering experts from different European countries
Tender process. Banedanmark (and PA Consulting Group)
Mathematical compendium. Banedanmark
Close corporation with
Prognoz in developing
and understanding
financial models and
algorithms.
Short clarification period. Banedanmark and Prognoz
Extensive use of
internal resources.
Development of IT-tool. Banedanmark and Prognoz
Team with high
mathematical and
technical skills.
Validation of the model. Banedanmark and Civity
Calibration of the model. Bandanmark – ongoing process
6
Prognoz
World wide IT company making Business Analytic Tools
Originates from research teams at Economic Cybernetics Department,
Perm State University, and Institute of Russian Academy of Science
7
Methodology and structure of the models
8
The methodology
The Bath Tub model
Postponed renewal
Costs
Costs
Speed reduction
Costs
Renewal costs
Maintenance
costs
Lifetime
Time of
investment/
reinvestment
Lifetime
Optimal time of
investment/
reinvestment
Optimal time of
renewal
In
•
•
•
Optimal time of
renewal
Postponed time
of renewal
case of postponed renewal:
Increased maintenance costs
Increased renewal costs
Risk of speed reductions (traffic hindrance costs)
In case of early renewal:
• Penalty
9
Model structure
Technical engineering relationships:
• Deterioration of the assets described by Service Life
relationship.
• Maintenance activities described according to European
models for tracks. For bridges inspections every 6 years.
Simple models for maintenance of catenary.
Finacial optimization for each line
section without constraints (only a
renewal pause)
Prognoz and Banedanmark
Banedanmark and European experts
Sub models
Inputdata
• Source data
• Model parameters
Technical
optimality
Unconstrained
LCC-optimality
Constrained
LCC-optimality
Financial optimization for each line section
with constraints, e.g.:
• Total renewal budget
• Total activity budget
Prognoz and Banedanmark
10
Sub models
Inputdata
Technical
optimality
LCC-optimality
Constrained
LCC-optimality
Inputdata
–
Detailed description of the infrastructure (geography)
–
Asset data, i.e. installation year, type of component, place of
component
–
Service life relationships curves for tracks and catenary
–
Condition data for bridges and tracks
–
Traffic data (load and number of passages)
–
Prices for renewal and maintenance activities
–
Interest rate and other parameters
11
Sub models
Inputdata
Technical
optimality
LCC-optimality
Constrained
LCC-optimality
Technical optimality
Results:
Technical optimal time for renewal of each component in
track/bridge/catenary.
Time of renewal is calculated from Service Life Relationship for
different types of components. Service Life Relationship are based
on European experience.
12
Sub models
Inputdata
Technical
optimality
LCC-optimality
Constrained
LCC-optimality
LCC-optimality
Results:
Technical and financial optimal time for renewal of each component
in track/bridge/catenary.
Optimization based on bundling of renewal activities on each line
section while minimizing total cost (renewal, maintenance, socioeconomic and penalty).
Optimization based on calculated Net Present Value
13
Sub models
Example - track
KM
Rail
0
1
1966
Sleeper
Installation date (inputdata)
2
3
4
1968
1966
1964
1987
Ballast
Technical
optimality
Inputdata
1987
5
1987
KM
Rail
0
1
2026
Sleeper
1998
Constrained
LCC-optimality
LCC-optimality
Technical optimal
2
3
4
2028
2026
2024
2027
Ballast
2027
5
2027
2028
Consequence: Renewal each year
KM
Rail
Sleeper
Ballast
0
1
2026
Technical optimal
2
3
4
2028
2026
2024
2027
2027
2028
5
2027
KM
Rail
Sleeper
Ballast
0
1
2027
LCC- optimality
2
3
4
2027
2027
2027
2027
2027
5
2027
2027
Consequence:
Activities are bundled
No traffic disturbance between renewal pauses
14
Sub models
Inputdata
Technical
optimality
LCC-optimality
Constrained
LCC-optimality
Constrained
LCC-optimality
Results:
Technical and financial optimal time for renewal
of each component in track/bridge/catenary
subject to constraints.
Optimization based on calculated Net Present
Value.
Optimizing:
• Minimize total costs
• Financial, activity or traffic constraints
• Priority (traffic importance) of each line section
Priority of line sections
15
Using the models and next steps
16
Using the models
TAM 1, TAM 1.1
• Political agreement for funding 2007-2014
TAM 2
• Political agreement for funding 2015-2020
• Yearly process of prioritization of renewal activities for a 4 year plan
• Yearly update of long term strategic plan (-2030)
• Dialog of yearly budget for maintemance
BAM 1
• Political agreement for funding 2015-2020
• Yearly process of prioritization of renewal activities for a 4 year plan
• Yearly update of long term strategic plan (-2030)
CAM 0.1-1
• Yearly process of prioritization of renewal activities for a 4 year plan
• Tender process for electrification of large parts of the infrastructure
17
Benefits
-
Extra 700 mio. DKK pr. year in 2015-2020
More transparent dialog of where and why the money is needed
Long term evaluation of investments
Use of Asset Management System
Common understanding between technical parts and financial parts of the
organization
- Identification of the removal of backlog, and more awareness of the long term
consequences of backlog
Next steps
–
Getting from CAM 0.1 to CAM 1
–
Further calibration of the models
–
Use in tender process (large scale projects)
–
Use for analysis purposes
18
Thank you
Contact information:
Dorte-Lene Bacher, [email protected]
19