A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

Chootinan, Piya; Chen, Anthony; Horrocks, Matthew R.; Bolling, Doyt

doi:10.1016/j.tra.2005.12.003

Cited by 85 publications

(54 citation statements)

References 8 publications

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“…There are more applications in maintenance including designing a risk-informed balanced system using a genetic algorithm (Podofillini & Zio 2008) , studies on optimising outage maintenance schedule (Hadavi 2008), selecting optimal repair and rehabilitation methods for reinforced concrete bridge decks (Lee & Kim 2007), multi-year preventive maintenance program (Chootinan et al 2006) and a model for preventive maintenance planning (Lapa et al 2006). There has also been interest in application of GAs in fault diagnosis including power transformer (Fei & Zhang 2009), water hydraulic motor ) and gas turbine (Ogaji et al 2005b).…”

Section: Gas In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Gas In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Assigning highway rehabilitation activity to any deteriorating pavement contributes to the improvement of pavement conditions (Chootinan et al 2006;Giustozzi et al 2012; Irfan et al 2008). The IRI index is therefore expected to decrease after applying the rehabilitation treatment (Lidicker et al 2012).…”

Section: Model Developmentmentioning

confidence: 99%

“…As already known, as rehabilitation helps resetting or recovering the deterioration process of a highway pavement (Irfan 2010), different treatments will affect the pavement performance differently (Chootinan et al 2006). This confirms the significance of highway treatment selection in pavement deterioration and therefore energy consumption (Zhang et al 2012).…”

Section: Model Developmentmentioning

confidence: 99%

Maximizing Environmental Sustainability and Public Benefits of Highway Construction Programs

Limsawasd¹

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“…Several issues were tackled by GAs because this approach was considering performing in order mainly to solve problems of resources allocation for road pavement maintenance and rehabilitation [2], [3], [4], [5], [6], [7], [8] but also to improve pavement design and analysis [9], [10], [11], [12], [13], [14], [15] and highway alignment design [16]. In particular, the research addressed to the pavement management system (PMS) was performed with continuity over the last two decades.…”

Section: Introductionmentioning

confidence: 99%

“…The optimization process was faced by opportunely programming GAs procedure according to two indicators: sideway force coefficient and predicted accident, both defined through models elaborated with ANN. Chootinan et al [5] dealt with a stochastic simulation-based GAs approach addressed to a multi-year pavement maintenance program. The GAs approach were used for handling the combinatorial nature of the network level pavement programming while a stochastic simulation was performed in order to take in account the uncertainty due to entity of traffic on the predicted pavement deterioration.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network

Mino¹,

Blasiis²,

Noto³

et al.

Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineeri

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Maintenance and improvement, through the rehabilitation, of the road infrastructure is a strategic and priority objective for road agencies, nevertheless the economic resources required are often inadequate. Within road management, the pavement management system (PMS) plays an essential role because of both the money needed and the performance that should be provided in terms of safety, ride quality and transport cost. The PMS is based on searching for a balanced solution between the lowest cost and the increased level of performance (i.e. pavement condition). In this paper a PMS multi-objective optimization method, was proposed, using a genetic algorithm (GA) to identify the best solution considering different rehabilitation strategies. The multi-objective optimization GA permits a set of optimal solutions (the Pareto solution set) that takes into account all the considered constraints. Finally on the basis of a specific criteria the best solution was selected in relation to the ranking of the priorities of the agency. A detailed numerical study was conducted on the Italian A18 motorway and the results showed that the proposed model PMS-GA is a suitable support to the decision making process.

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A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

Cited by 85 publications

References 8 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Maximizing Environmental Sustainability and Public Benefits of Highway Construction Programs

An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network

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