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A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning

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  • Zhang, Le
  • Fu, Liangliang
  • Gu, Weihua
  • Ouyang, Yanfeng
  • Hu, Yaohua

Abstract

We formulate a general bottom-up model for the joint optimization of maintenance, rehabilitation, and reconstruction (MR&R) schedules for a system of heterogeneous pavement segments under budget constraints. The objective is to minimize the total costs incurred to both the highway users and the pavement management agency. We propose a Lagrange multiplier approach together with derivative-free quasi-Newton algorithms to solve the problem for two scenarios: i) with a combined budget constraint for all the treatments; and ii) with one budget constraint for each treatment. The system-level solution approach has the following merits: i) it can be applied to problems with any forms of segment-level models for user and agency costs, deterioration process, and treatment effectiveness, given that the solution to the segment-level problem is available; ii) under the combined budget constraint, it ensures that the optimality gap of the system-level solution is bounded by a term that depends upon the optimality gap of the segment-level solutions; and iii) it exhibits linear complexity with the number of segments.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:105:y:2017:i:c:p:378-400
    DOI: 10.1016/j.trb.2017.09.014
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    References listed on IDEAS

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    3. 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.

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