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Model Uncertainty and the Management of a System of Infrastructure Facilities

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  • Kuhn, Kenneth D.
  • Madanat, Samer M.

Abstract

The network-level infrastructure management problem involves selecting and scheduling Maintenance, Repair, and Rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov Decision Problem (MDP) solved via Linear Programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using Robust Optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.

Suggested Citation

  • Kuhn, Kenneth D. & Madanat, Samer M., 2005. "Model Uncertainty and the Management of a System of Infrastructure Facilities," University of California Transportation Center, Working Papers qt6c84b9b4, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt6c84b9b4
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    Cited by:

    1. Sathaye, Nakul & Madanat, Samer, 2011. "A bottom-up solution for the multi-facility optimal pavement resurfacing problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1004-1017, August.
    2. Durango-Cohen, Pablo L. & Madanat, Samer M., 2008. "Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(8), pages 1074-1085, October.
    3. Xinhua Mao & Changwei Yuan & Jiahua Gan, 2019. "Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model," Sustainability, MDPI, vol. 11(9), pages 1-15, April.
    4. Orcesi, André D. & Cremona, Christian F., 2010. "A bridge network maintenance framework for Pareto optimization of stakeholders/users costs," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1230-1243.
    5. Zhang, Le & Fu, Liangliang & Gu, Weihua & Ouyang, Yanfeng & Hu, Yaohua, 2017. "A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 378-400.
    6. Seyedshohadaie, S. Reza & Damnjanovic, Ivan & Butenko, Sergiy, 2010. "Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 236-248, May.
    7. Charles-Antoine Robelin & Samer M. Madanat, 2008. "Reliability-Based System-Level Optimization of Bridge Maintenance and Replacement Decisions," Transportation Science, INFORMS, vol. 42(4), pages 508-513, November.
    8. Lee, Jinwoo & Madanat, Samer, 2015. "A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 106-122.
    9. Shi, Yue & Xiang, Yisha & Xiao, Hui & Xing, Liudong, 2021. "Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems," European Journal of Operational Research, Elsevier, vol. 288(2), pages 382-393.
    10. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Sathaye, Nakul & Madanat, Samer, 2012. "A bottom-up optimal pavement resurfacing solution approach for large-scale networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 520-528.

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