Maintenance optimization of infrastructure networks using genetic algorithms

Morcous, George; Lounis, Z

doi:10.1016/j.autcon.2004.08.014

Cited by 163 publications

(72 citation statements)

References 14 publications

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“…However, there are similarities between maintenance principles for different types of road infrastructure. For example, Morcous and Lounis (2005) apply the same maintenance optimisation techniques to bridges and Grivas et al (1993) to concrete pavements as other authors apply to flexible pavements. Road maintenance can be categorised as:…”

Section: Mark O Harvey -Discussion Paper 2012-12 -© Oecd/itf 2012mentioning

confidence: 99%

“…Paterson and Attoh-Okine (1992) Morcous and Lounis (2005) discuss the advantages of stochastic models over deterministic models, including better handling of uncertainties, consideration of current pavement conditions in predicting future conditions, and practicality in dealing with largesized networks due to their computational efficiency and simplicity of use. Hence, they are typically applied for estimating long-term budgets and making needs projections at the network level.…”

Section: Probabilistic Approachesmentioning

confidence: 99%

“…Transition probabilities are obtained from past data or expert judgement (Li et al 1997, Morcous andLounis 2005). In the absence of any treatment, a pavement can only remain in the same state for deteriorate to a lower state, never rise to a higher state.…”

Section: Probabilistic Approachesmentioning

confidence: 99%

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Optimising Road Maintenance

Harvey¹

2012

International Transport Forum Discussion Papers

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Section: Mark O Harvey -Discussion Paper 2012-12 -© Oecd/itf 2012mentioning

confidence: 99%

Section: Probabilistic Approachesmentioning

confidence: 99%

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Optimising Road Maintenance

Harvey¹

2012

International Transport Forum Discussion Papers

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“…Miyamoto et al, [6] described the GA as a power tool for obtaining optimal maintenance plans. Other work using this approach for maintenance optimization is carried out by Liu and Frangopol [7], Furuta et al [8] and Morcous and Lounis [9]. In the present application the developed GA based optimization methodology combines probabilistic effectiveness modelling of PM with the cost of different PM measures to identify optimum strategies that will combat the corrosion deterioration of RC structures.…”

Section: Ga Based Methodology For Optimum Pm Strategiesmentioning

confidence: 99%

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Tantele¹,

Votsis²,

Onoufriou³

2014

OJCE

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Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.

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“…The major barrier of the optimization problem is that the solution space grows exponentially with the size of the problem, so the conventional optimization approach can be inefficient to find the optimal solution. However, GAs, which incorporate a set of initial solutions and generate new and better solutions according to the probabilistic rules, can be more effective and the likelihood of achieving the optimal solution is increasing (Morcous & Lounis, 2005).…”

Section: Introductionmentioning

confidence: 99%

Road maintenance planning using network flow modelling

Yang

Remenyte‐Prescott

Andrews

2015

IMA Journal of Management Mathematics

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This paper presents a road maintenance planning model that can be used to balance out maintenance cost and road user cost, since performing road maintenance at night can be convenient for road users but costly for highway agency. Based on the platform of the network traffic flow modelling, the traffic through the worksite and its adjacent road links is evaluated. Thus, maintenance arrangements at a worksite can be optimized considering the overall network performance. In addition, genetic algorithms are used for maintenance planning in order to find the best maintenance arrangements for the worksites. The key variables in the optimization model involve the starting time of maintenance works during the day, their duration, the duration of the break during the maintenance work and traffic signal controls at the worksite.

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Maintenance optimization of infrastructure networks using genetic algorithms

Cited by 163 publications

References 14 publications

Optimising Road Maintenance

Optimising Road Maintenance

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Road maintenance planning using network flow modelling

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