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Integrated Multi-Criteria Budgeting for Maintenance and Rehabilitation Policies at the Finnish Road Administration. Pekka Mild and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology (TKK) P.O. Box 1100, 02015 TKK, Finland. Finnish Road Administration (Finnra) - PowerPoint PPT Presentation
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Helsinki University of Technology Systems Analysis Laboratory
INFORMS Seattle 2007
Integrated Multi-Criteria Budgeting for Integrated Multi-Criteria Budgeting for Maintenance and Rehabilitation Policies Maintenance and Rehabilitation Policies
at the Finnish Road Administrationat the Finnish Road Administration
Pekka Mild and Ahti SaloSystems Analysis Laboratory
Helsinki University of Technology (TKK)
P.O. Box 1100, 02015 TKK, Finland
Helsinki University of Technology Systems Analysis Laboratory
2INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Road asset management in FinlandRoad asset management in Finland
Finnish Road Administration (Finnra)– Central administration and 9 road districts
– Maintenance, repair and investments mgmt
– Research and development
Road network– 78000 km of public roads
– 14000 bridges
Estimated asset value 21 billion USD– Around 4000 USD per capita
– Annual funding around 850 million USDPave-ments
Brid-ges
Gravel Roads
Road Equip-ment
OtherRoad Assets
ROAD ASSET MANAGEMENT
DATA
UTILISATION
Data collection and manage-ment
I
II
III
IV
Road Asset Management methodology
Utilisation of road manage-ment data
Total Highway Manage-ment
METHODS
INFRASTRUCTURE ASSETS
Pave-ments
Brid-ges
Gravel Roads
Road Equip-ment
OtherRoad Assets
ROAD ASSET MANAGEMENT
DATA
UTILISATION
Data collection and manage-ment
I
II
III
IV
Road Asset Management methodology
Utilisation of road manage-ment data
Total Highway Manage-ment
METHODS
INFRASTRUCTURE ASSETS
Road asset management researchprogram 2003-2007
Helsinki University of Technology Systems Analysis Laboratory
3INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Programmed rehabilitation and reconstruction projects
Pavements Bridges Gravel roads Road equipment
Day-to-day maintenance operations
Winter-time operations Road surroundings Gravel roads
How to allocate funds among road keeping products?How to allocate funds among road keeping products?
Finnra must address multiple objectives in its policies• Shift from technical maintenance to customer and service orientation
• New unified quality classes map levels of service
All products impact the same road system• No integrated management system to-date → static funding patterns
• Yet, sustainable development calls for dynamic (re)allocations
Build an integrated framework for resource allocation• Multi-criteria framework as the ”common language” among products
• Bring managers together to address future funding needs
Helsinki University of Technology Systems Analysis Laboratory
4INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Road district’s annual rehabilitation and maintenance budget
Programmed rehabilitationand reconstruction projects
Day-to-day roadmaintenance operations
Pavements Bridges Gravelroads
Roadequipment
Winter-time
Road sur-roundings
Gravel-roads
... 5 quality classes for all twig-level products1 2 3 4 5
High traf. Low ... 1-3 sub-categories per product type => altogether 13 twig-level products compete for funding
Road products and evaluation criteriaRoad products and evaluation criteria
ROAD SAFETY
ENVIRONMENTAL IMPACTCUSTOMER SATISFACTION
ASSET VALUE PRESERVATION
Helsinki University of Technology Systems Analysis Laboratory
5INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Value-focused evaluation of products Value-focused evaluation of products
TKK-facilitated one-day workshop – 10 experts from Finnra and Pöyry Infra Ltd.
Score elicitation– Intermediate scores by adjusting the shape of
the value functions for each product
– Maximum scores by comparing inter-product swings from the worst quality class to the best
– These two phases repeated for all four criteria
Weight elicitation– Incomplete rank information about maximum
swings under each criterion
)( jvik
1 2 3 4 5 class (j)
100
0
50
Customer satisfaction
bridges
)( jvik
1 2 3 4 5 class (j)
100
0
50
Customer satisfaction
bridges
winter mnt.
gravel rd.
Helsinki University of Technology Systems Analysis Laboratory
6INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Aggregate multicriteria value of productsAggregate multicriteria value of products
1 2 3 4 5
quan
tity
(qj)
class (j)
bridges: quality class distribution
j
ij
ik
ik tqjvtV )()()(
• Score times quantity
1 2 3 4 5 1 2 3 4 51 2 3 4 5
Road safety Environmental impactAsset value
1 2 3 4 5
Customer satisfaction
bridges
bridgesbridges
bridges
)( jvik )( jvik )( jvik )( jvik
2007
Year
)(tV i
k
ikk
i tVwtV )()(
• Weighted sum of scores
Helsinki University of Technology Systems Analysis Laboratory
7INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Deterioration and repair dynamics of productsDeterioration and repair dynamics of products
1 2 3 4 5
quan
tity
(qj)
class (j)
2007
Yr.
)(tV ik
1 2 3 4 5
t + 1
$
2007
Yr.
)(tV i
t + 1
2008
1 2 3 4 5
t + n
t + n
2007
Yr.
)(tV i
2008
2009
2010
2011
2012
2037
…
…
…
Products deteriorate towards worse quality classes over time Repairs raise quality
Helsinki University of Technology Systems Analysis Laboratory
8INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Optimal resource allocationsOptimal resource allocations
Maximize the long-term sum of all products’ multicriteria value– Time horizon of 30 years with 3% p.a. discount rate
– Budget constraints and quality targets
– Decision variables: repair actions and levels of maintenance operations» Number of quality class 1 bridges repaired to class 4 in year 2008
» Kilometers held at winter maintenance quality class 3 in year 2012
– Repair and deterioration dynamics captured by linear constraints
Different weights suggest different optimal allocations – Sample the feasible weight set determined by the rank-ordering
Helsinki University of Technology Systems Analysis Laboratory
9INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Key results for managementKey results for management
Which resource allocation policies maximize the long-term multicriteria value of the whole road system?– Which products call for more funding when customer satisfaction
becomes a key priority?
– What do criteria weightings imply for the products’ funding needs?
– What is the expected interim/terminal quality distribution of the system?
What is the ”pecking order” of the products?– Which products gain/lose funding when the overall budget is changed?
– Which products gain/lose funding first and which later?
– What do different weightings imply for the ”pecking order”?
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Year
Allo
cate
d to
pro
duct
cat
egor
y M
€
MIN: Bridges
AVE: Bridges
MAX: Bridges
AVE: Pavements
MIN: Pavements
MAX: Pavements
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
Cur
rent
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Year
Allo
cate
d to
pro
duct
cat
egor
y [M
€]
Road equipment (prog.)
Winter-time (maint.)
Bridges (prog.)
Road surroundings (maint.)
Gravel roads (maint.)
Gravel roads (prog.)
Pavements (prog.)
Computed
Helsinki University of Technology Systems Analysis Laboratory
10INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Integrated platformIntegrated platformfor collaborative for collaborative management of management of the entire systemthe entire system
$
……
Helsinki University of Technology Systems Analysis Laboratory
11INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Client feedbackClient feedback
Best project award in Finnra’s road asset management research program
”An innovative tool for thinking and communication” – Antti Rinta-Porkkunen, Director of the South-East Finland road district
”Framework to bring the managers of separated products to facilitated interaction and give them fresh insights about the aggregate system” – Vesa Männistö, Senior Consultant, Pöyry Infra Ltd.
Enthusiasm for optimization and decision analysis at Finnra
Helsinki University of Technology Systems Analysis Laboratory
12INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Novel methodological elements in our caseNovel methodological elements in our case
From technical condition-focus to value-focus– Explicit value models for quality classes
From product orientation to portfolio optimization – Incomplete preference information through rank-orderings
From static budgeting to long-term allocations – Integrated repair and deterioration dynamics of products
From turf-fights to collaborative learning – Interactive work-shop with ’on-the-fly’ computations
Helsinki University of Technology Systems Analysis Laboratory
13INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Towards integrated sustainable planning Towards integrated sustainable planning
Infrastructure & transportation asset management – Consumes enormous financial resources globally
– Has far-reaching impacts on societies, industries and individuals
– Involves multiple objectives, long planning horizons, high uncertainties
There is major untapped potential for Decision Analysis– Value-focused analysis of individual products and product portfolios
– Explicit recognition of stakeholders’ interests and preferences
– Use of DA models as vehicles for enhanced communication
– A paradigm shift towards integrated collaborative planning
Helsinki University of Technology Systems Analysis Laboratory
14INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Thank you!Thank you!
Questions?Questions?
Helsinki University of Technology Systems Analysis Laboratory
15INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Appendix: LP-model formulation (1/3), variables & dynamicsAppendix: LP-model formulation (1/3), variables & dynamics Decision variables (product i, class j, year t)
– Quantity distribution:
– Amount (kilometers, units) moved from j to j’: ,0)(' txi jj
Linear repair and deterioration dynamics– Percentage of quantity deteriorates, i.e., drops to in one year– for all maintenance operations products– Linear constraints
– Slightly different constraints for boundary states (1 and 5)– Set of allowed state transitions can be restricted product-wise
,0)( tq ij j
ij
j
ij tqtq )()( 0
)()('
' tqtx ij
j
ijj
ijd )(tq ij )(1 tq ij
0ijd
jj
ijj
jj
ijj
ij
ij
ij
ij
ij txtxtqdtqdtq
''
''11 )()()()()1()1(
Remains in class j Deteriorates from class j+1
Moved upwardsfrom j to j’
Moves that arrive at j from below j’
Helsinki University of Technology Systems Analysis Laboratory
16INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Appendix: LP-model formulation (2/3), objective functionAppendix: LP-model formulation (2/3), objective function
Evaluation score (product i, class j, criterion k)
Value of distribution (product i, criterion k, year t)– qij(t): quantity of product i in class j in year t
Overall value of distribution (product i, year t)– wk: weight of criterion k (incomplete weighting wSw)
Overall value of all products (year t)– Sum of all products’ distributions’ overall values
Total overall value discounted over 30 years– Objective function in the optimization
j
ij
ik
ik tqjvtV )()()(
)( jvik
i
i tVtV )()(
k
ikk
i tVwtV )()(
t
t tVrV )(1
Helsinki University of Technology Systems Analysis Laboratory
17INFORMS Seattle 2007, Pekka Mild and Ahti Salo
Appendix: LP-model formulation (3/3), costs & constraintsAppendix: LP-model formulation (3/3), costs & constraints
Costs– Programmed repairs (i REP): unit cost per move is
– Maintenance operations (i MNT): unit cost of service level is
– for i MNT (shifts are free but the resulting quantity comes to cost)
)()1()1()()1( tqtqtq ij
ij
ij
ijjc ')(' txi jj
)(tq ij ijc
Budget constraints
– Budget constraints can be set also for any subsets of products or moves
0' i
jjc
BtqctxcjMNTi
ij
ij
jjREPi
ijj
ijj
,',,'' )()(
Examples of other constraints– Gradual change
– (Dynamic) target thresholds for distributions
– E.g., share of poor-conditioned (class 1) bridges must be below 1% in year 2015)()()( max,min, tqtqtq i
jij
ij