A Bootstrap-Based Integer Programming Algorithm for Budget Allocation in Pavement Management Systems

Abukhalil, Yazan; Smadi, Omar

doi:10.1061/(asce)is.1943-555x.0000663

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The area between the improved condition curve and the condition deterioration curve (no treatment) in Fig 1 is defined as the condition improvement area. M&R benefit of segment j, B j , is calculated as the product of Annual Average Daily Traffic (AADT), segment length and condition improvement area using Eq (6). To enhance the investment return, the maximization of the total M&R benefit is defined as the second objective as presented in Eq (2).…”

Section: Decision-making Modelmentioning

confidence: 99%

“…A high annual budget may not be efficient considering the strained finances; and a low annual budget causes the delays on M&R treatments, which contributes to the accumulation of yearly M&R backlogs and accelerates the road deterioration [ 4 , 5 ]. According to Abukhalil and Smadi, the scarcity of funding on M&R backlog had increased from $549.5 billion in 2009 to $836 billion in 2017 [ 6 ]. Therefore, an appropriate annual budget should be sufficient to allow “the right treatment on the right pavement at the right time” [ 7 ] and result in the highest investment return.…”

Section: Introductionmentioning

confidence: 99%

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Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

Chen

2023

PLoS ONE

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Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management.

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Section: Decision-making Modelmentioning

confidence: 99%