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

1. 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

2. 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 USD Road asset management in Finland Road asset management researchprogram 2003-2007

3. Pavements Bridges Gravel roads Road equipment • 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 Programmed rehabilitation and reconstruction projects Day-to-day maintenance operations • Build an integrated framework for resource allocation • Multi-criteria framework as the ”common language” among products • Bring managers together to address future funding needs Winter-time operations Road surroundings Gravel roads How to allocate funds among road keeping products? • All products impact the same road system • No integrated management system to-date → static funding patterns • Yet, sustainable development calls for dynamic (re)allocations ? ? ?

4. Programmed rehabilitationand reconstruction projects Day-to-day roadmaintenance operations Pavements Bridges Gravelroads Roadequipment Winter-time Road sur-roundings Gravel-roads High traf. Low ... 1-3 sub-categories per product type => altogether 13 twig-level products compete for funding ...5 quality classes for all twig-level products 1 2 3 4 5 ASSET VALUE PRESERVATION ROAD SAFETY ENVIRONMENTAL IMPACT CUSTOMER SATISFACTION Road products and evaluation criteria Road district’s annual rehabilitation and maintenance budget

5. Customer satisfaction 100 bridges 50 0 class (j) 1 2 3 4 5 Customer satisfaction 100 winter mnt. bridges 50 gravel rd. 0 class (j) 1 2 3 4 5 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

6. bridges bridges bridges bridges quantity (qj) 1 2 3 4 5 Road safety Customer satisfaction class (j) 1 2 3 4 5 Environmental impact Asset value • Score times quantity 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 • Weighted sum of scores Year 2007 Aggregate multicriteria value of products bridges: quality class distribution

7. quantity (qj) t + n t + n t + 1 t + 1 1 2 3 4 5 $ class (j) 1 2 3 4 5 1 2 3 4 5 … … … Yr. Yr. 2007 2007 2008 2009 2010 2011 2012 2037 Yr. 2007 2008 Deterioration and repair dynamics of products • Products deteriorate towards worse quality classes over time • Repairs raise quality

8. Optimal 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

9. Key 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”?

10. $ … … Integrated platformfor collaborative management of the entire system

11. Client 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

12. Novel 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

13. 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

14. Thank you!Questions?

15. Moves that arrive at j from below j’ Moved upwards from j to j’ Remains in class j Deteriorates from class j+1 Appendix: 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’: • 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

16. Appendix: 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 wSw) • 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

17. Appendix: 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) • Budget constraints • Budget constraints can be set also for any subsets of products or moves • 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