Pekka Mild and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology (TKK)

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


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



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



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



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


bridges

winter mnt.

gravel rd.



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


bridges

bridgesbridges

bridges

)( jvik )( jvik )( jvik )( jvik

2007

Year

)(tV i

k

ikk

i tVwtV )()(

• Weighted sum of scores



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



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



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

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2007

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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

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Cur

rent

2007

2008

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2012

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2015

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2018

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2020

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2026

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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



Integrated platformIntegrated platformfor collaborative for collaborative management of management of the entire systemthe entire system

$

……



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



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



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



Thank you!Thank you!

Questions?Questions?



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’



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



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

Documents

Pekka Mild and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology (TKK)