A Multi-Objective Optimization Framework for Capital Allocation in Public Infrastructure Systems

Introduction: Public infrastructure agencies must allocate limited capital across heterogeneous assets while balancing lifecycle cost, safety and reliability risk, service criticality, and governance constraints. Conventional composite scoring and single-objective ranking can obscure trade-offs and embed implicit value judgments.
Methods: We formulate capital allocation as a constrained multi-objective optimization problem that maximizes portfolio-level expected risk reduction and service criticality subject to a budget cap and a minimum compliance requirement. Pareto-optimal portfolios are generated to expose trade-offs, and a transparent, Pareto-first balanced selection rule is used to recommend one portfolio without obscuring alternatives. A reproducible synthetic portfolio (N=16) is specified via distributions and random seed; a composite-score baseline is implemented for comparison; sensitivity and robustness analyses vary budget, compliance, and key input perturbations. A scalability demonstration (N=500) uses an NSGA-II-style heuristic with random-key encoding and constraint repair to generate an approximate frontier.
Results: For N=16, Pareto analysis yields multiple defensible portfolios that differ materially by governance preference. A balanced portfolio is selected by a mathematically defined minimum utopia-point distance rule on normalized objectives, and an alternative knee-point rule is reported for sensitivity. Compared with composite scoring under the same constraints, Pareto-based reporting exposes dominated (inefficient) choices and improves transparency. For N=500, the heuristic generates an approximate frontier in seconds, illustrating feasibility at realistic portfolio scale.
Conclusions: The framework shifts capital planning from static prioritization to explainable optimization aligned with asset management guidance. It supports auditable decision-making under real governance constraints and is implementable within enterprise asset management and decision-support platforms.

multi-objective optimization; transportation

1. AASHTO. Transportation Asset Management Guide: A Focus on Implementation. American Association of State Highway and Transportation Officials; 2011. [Google Scholar] [Crossref]

2. FHWA. Transportation Asset Management Guide: A Focus on Implementation (HIF-13-047). Federal Highway Administration; 2013. [Google Scholar] [Crossref]

3. FHWA. Asset Management Overview: Strategies for Implementation (tradeoff analysis and project selection). Federal Highway Administration; 2008. [Google Scholar] [Crossref]

4. Transportation Association of Canada (TAC). Performance-Based Decision Making for Asset Management. TAC; 2021. [Google Scholar] [Crossref]

5. Bai Q, et al. Trade-Off Analysis for Multiobjective Optimization in Transportation Asset Management. Transportation Research Record; 2012. [Google Scholar] [Crossref]

6. Chen L, et al. Optimization in decision making in infrastructure asset management: a review. Applied Sciences. 2019;9(7):1380. [Google Scholar] [Crossref]

7. Keeney RL, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley; 1976. [Google Scholar] [Crossref]

8. Bai Q, et al. Methodology for analyzing the trade-offs associated with multi-objective optimization in transportation asset management under uncertainty. Computer-Aided Civil and Infrastructure Engineering. 2021. [Google Scholar] [Crossref]

9. Mrawira DM. Cross-Assets Trade-off Analysis: Why Are We Still Talking About It? Transportation Research Board (TRB) Annual Meeting paper; 2009. [Google Scholar] [Crossref]

10. TAM Portal. Defining Cross-Asset Decision Making: A Discussion Paper (Cross-Asset Allocation). 2016. [Google Scholar] [Crossref]

11. Rueda-Benavides J, et al. Cross-asset prioritization models and trade-offs in transportation project selection. International Journal of Sustainable Transportation. 2023. [Google Scholar] [Crossref]

12. ISO. ISO 55000: Asset management—Overview, principles and terminology. International Organization for Standardization; 2024. [Google Scholar] [Crossref]

13. Branke J, Deb K. Finding knees in multi-objective optimization. Parallel Problem Solving from Nature (PPSN). 2004. [Google Scholar] [Crossref]

14. Li W, et al. Knee point-guided multiobjective optimization algorithm. Mathematical Problems in Engineering. 2020. [Google Scholar] [Crossref]

15. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 2002;6(2):182–197. [Google Scholar] [Crossref]

16. Petchrompo S, et al. Pruning Pareto optimal solutions for multi-objective budget allocation problems. European Journal of Operational Research. 2022. [Google Scholar] [Crossref]

17. Naseri H, et al. Multi-objective evolutionary optimization applications in pavement management scenarios. Results in Engineering. 2024. [Google Scholar] [Crossref]

18. Pourgholamali M, et al. Multiobjective optimization in pavement management systems and related reviews. Journal of Road Engineering. 2023. [Google Scholar] [Crossref]

19. Zhang Y, et al. Multi-objective optimization for sustainable bridge repair and replacement strategies. Structure and Infrastructure Engineering. 2023. [Google Scholar] [Crossref]

20. Shoghli O. A decision support system for multi-objective multi-asset roadway asset management. Virginia Tech dissertation; 2014. [Google Scholar] [Crossref]

21. NCHRP. Incorporating Maintenance Costs into a Transportation Asset Management Plan: Summary. NCHRP Report 1076; 2023. [Google Scholar] [Crossref]

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