Genetic algorithm used in simulation model: Application in a maintenance process

Khebbache-Hadji, Selma; Hani, Yasmina; Lahiani, Nouha; Mhamedi, Abderrahman El

doi:10.3182/20120523-3-ro-2023.00381

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…GAs are a type of evolutionary algorithm commonly used to solve real-world problems which may be intractable or involve messy, highly non-linear systems (Huband et al, 2003). Evolutionary algorithms are easy to integrate with DES software (Khebbache-Hadji et al, 2012), can search large solution spaces (Marseguerra & Zio, 2000) without getting stuck at local optima (Allaoui & Artiba, 2004;Alrabghi & Tiwari, 2015), can solve multi-objective problems (Moradi et al, 2011), and are often able to solve problems without prior knowledge of the response of the system (Biethahn & Nissen, 1994;Alrabghi & Tiwari, 2015). As a result they have a history of being used successfully to solve challenging problems (Ng et al, 2009;Paulo et al, 2016;Sanchez et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

“…While the number of papers that simulate real case studies has increased, many modelers limit their applications to relatively simple systems. Examples include a single asset (Lhorente et al, 2003), many units of the same asset-type (Bazargan & McGrath, 2003;Berquist et al, 2002;Khebbache-Hadji et al, 2012;Moghaddam, 2013), a single asset with several components (Paulo et al, 2016), or several different assets (Kortelaimen et al, 2000;Louit & Knights, 2001). Nowakowski & Werbinka (2009) explored multi-component systems and their interactions, noting that while this complicates the modelling and optimization process, it also offers the opportunity to group maintenance actions and thus limit the effects of maintenance on system performance.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing fixed-interval maintenance periods for mobile asset sub-systems operating in remote locations

Hodkiewicz

Bonser²,

Chin³

et al. 2018

PHME_CONF

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Fixed-interval planned maintenance is the predominant strategy for looking after mobile asset fleets such as cars, trains, and trucks. To understand the impact of maintenance interval selection on the fleet's performance it is necessary to mathematically link these planned maintenance tasks to the asset's subsequent reliability performance and the ability to complete planned journeys without incident. Failures, particularly those happening in remote locations, are costly to manage, cause significant operational disruption, and increase safety exposure for maintainers. A typical mobile asset has some number of sub-systems, each with its own maintenance interval and reliability. Our challenge is to set appropriate maintenance intervals for each asset sub-system to minimize the probability of an unplanned failure, without limiting unnecessarily the availability of the asset. To do this we generate sets of maintenance intervals using a genetic algorithm and test them using a discrete event simulation (DES) model of the operations and maintenance functions. We are motivated by two industry examples of fleets in remote locations: long-distance freight trucks, and heavy-haulage rail locomotives. In the truck case, the model found optimal intervals similar to those used by the operator. The locomotive case is more complex, but the model suggests improvements are possible in interval selection, maintenance practices, and data collection. Each model is conceptualized for its specific context; this process identifies assumptions that need to be considered when linking maintenance and operations models.

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