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Mathematical programming framework for modeling and comparing network-level pavement maintenance strategies

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  • Chu, James C.
  • Huang, Kai-Hsiang

Abstract

This study proposes a mathematical programming framework to model and quantitatively compare different maintenance strategies for network-level highway pavements. The study develops mixed-integer linear programming models for various maintenance strategies that are commonly adopted in practice and in the literature. In developing these models, traffic, pavement age, and maintenance actions with heterogeneous effects are considered. The strategies include optimization-based, worst-first, best-first, and threshold-based strategies. To demonstrate the flexibility of the framework and present a practical situation in which engineering judgment is sometimes incorporated in pavement maintenance strategies, we further develop a mixed strategy. A solution procedure combining the off-shelf mixed-integer programming solver, greedy algorithms, and Lagrangian relaxation algorithms is developed to efficiently solve the models. Finally, a numerical example of a hypothetical network is established. Different maintenance strategies are applied given different budget levels, traffic loadings, and initial pavement conditions. The results of the numerical example are reasonable, and they provide insights into the efficient implementation of maintenance strategies. Results also show that the framework has the potential to aid maintenance agencies in evaluating maintenance strategies before they are implemented, improving pavement conditions, and reducing the budget for transportation infrastructure.

Suggested Citation

  • Chu, James C. & Huang, Kai-Hsiang, 2018. "Mathematical programming framework for modeling and comparing network-level pavement maintenance strategies," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 1-25.
  • Handle: RePEc:eee:transb:v:109:y:2018:i:c:p:1-25
    DOI: 10.1016/j.trb.2018.01.005
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    References listed on IDEAS

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    1. 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.
    2. Ouyang, Yanfeng & Madanat, Samer, 2006. "An analytical solution for the finite-horizon pavement resurfacing planning problem," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 767-778, November.
    3. 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.
    4. Chu, James C. & Chen, Yin-Jay, 2012. "Optimal threshold-based network-level transportation infrastructure life-cycle management with heterogeneous maintenance actions," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1123-1143.
    5. Lee, Jinwoo & Madanat, Samer & Reger, Darren, 2016. "Pavement systems reconstruction and resurfacing policies for minimization of life‐cycle costs under greenhouse gas emissions constraints," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 618-630.
    6. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
    7. Gu, Weihua & Ouyang, Yanfeng & Madanat, Samer, 2012. "Joint optimization of pavement maintenance and resurfacing planning," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 511-519.
    8. 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.
    9. Lee, Jinwoo & Madanat, Samer, 2014. "Joint optimization of pavement design, resurfacing and maintenance strategies with history-dependent deterioration models," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 141-153.
    10. 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.
    11. Ouyang, Yanfeng & Madanat, Samer, 2004. "Optimal scheduling of rehabilitation activities for multiple pavement facilities: exact and approximate solutions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(5), pages 347-365, June.
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    Cited by:

    1. Takumi Asada & Tran Vinh Ha & Mikiharu Arimura & Shuichi Kameyama, 2022. "A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    2. 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.
    3. Yingnan Yang & Hongming Xie, 2021. "Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making," Sustainability, MDPI, vol. 13(5), pages 1-10, March.

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