Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty

Fani, Amirhossein; Golroo, Amir; MirHassani, S. A.; Gandomi, Amir H.

doi:10.1080/10298436.2020.1748628

Cited by 26 publications

(10 citation statements)

References 30 publications

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“…There has been an interest in developing simulation-based reinforcement models for maintenance planning to large networks (Memarzadeh and Pozzi, 2019). There are also other applications such as pavement maintenance (Fani et al , 2020), maintenance of hazardous systems (Nozhati et al , 2020). Figure 14 represents the historiographic direction citation network of the discussed papers published from years 1991 through 2017.…”

Section: Historiographical Analysismentioning

confidence: 99%

Models for maintenance planning and scheduling – a citation-based literature review and content analysis

Raza

Hameed

2021

JQME

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PurposeThe findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures.Design/methodology/approachWe have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies.FindingsBA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities.Originality/valueThere are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.

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Section: Historiographical Analysismentioning

confidence: 99%

Models for maintenance planning and scheduling – a citation-based literature review and content analysis

Raza

Hameed

2021

JQME

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“…al. [15] to find the optimal plan. García-Segura et al [16] have incorporated the uncertainty of pavement deterioration into pavement management optimization.…”

Section: Introductionmentioning

confidence: 99%

Exploring New Computational Strategies for Managing Maintenance Activities of Airport Pavement Systems

Ragusa

Costa

Graziano

2022

Int. J. Pavement Res. Technol.

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In this paper, the optimization of the maintenance management problem regarding airport pavements is addressed by developing a series of fast computational procedures whose rationales pursue both practical and economical objectives. Particularly, the study involves the design of five new heuristic algorithms replicating different management strategies that can be undertaken by the airport managers in a certain time horizon for reducing the pavement maintenance costs while considering the impact on the service level of the airport. Each algorithm works on the basis of a preliminary computational framework that has a twofold scope: (i) selecting the pavement portions to be maintained during the provided time horizon. (ii) using a K-means method based on three well-known performance indicators, which, namely, are Residual Life (RL), International Roughness Index (IRI) and Pavement Condition Index (PCI), to group the selected portions into homogenous clusters named work-zones. To evaluate the effectiveness and the efficiency of the proposed optimization algorithms in coping with the maintenance programming problem under investigation, an extended design of experiments based on international airport regulations has been arranged. The obtained numerical results revealed that no single strategy can be selected as the most performing in terms of cost and quality conditions of the pavements. To make robust the numerical results, a sensitivity analysis is conducted to evaluate the influence of the total cost of maintenance on runway length and number of sections. However, the results obtained in this study provide a series of managerial implications, further expanding the research contribution.

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“…This can subsequently lead to deterioration of road structures or pose challenges in the assessment of maintenance outcomes. Therefore, assessment and prediction of road deterioration [5] as well as establishment of the relationship between maintenance schemes and corresponding influencing factors [6] are crucial in decision making regarding pavement maintenance and fund allocation.…”

Section: Introductionmentioning

confidence: 99%

Pavement Maintenance Decision Making Based on Optimization Models

et al. 2021

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Pavement maintenance prioritization considering both quality and cost is an important decision-making problem. In this paper, the actual pavement condition index of city roads was calculated using municipal patrol data. A linear optimization model that maximized maintenance quality with limited maintenance costs and a multi-objective optimization model that maximized maintenance quality while minimizing maintenance costs were developed based on the pavement condition index. These models were subsequently employed in making decisions for actual pavement maintenance using sequential quadratic programming and a genetic algorithm. The results showed that the proposed decision-making models could effectively address actual pavement maintenance issues. Additionally, the results of the single-objective linear optimization model verified that the multiobjective optimization model was accurate. Thus, they could provide optimal pavement maintenance schemes for roads according to actual pavement conditions. The reliability of the models was investigated by analyzing their assumptions and validating their optimization results. Furthermore, their applicability in pavement operation-related decision making and preventive maintenance for roads of different grades was confirmed.

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Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty

Cited by 26 publications

References 30 publications

Models for maintenance planning and scheduling – a citation-based literature review and content analysis

Models for maintenance planning and scheduling – a citation-based literature review and content analysis

Exploring New Computational Strategies for Managing Maintenance Activities of Airport Pavement Systems

Pavement Maintenance Decision Making Based on Optimization Models

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