Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

Sancho, M. T.; Sánchez, Ana Isabel; Alberola, Sofía Carlos; Martorell, Sebastián

doi:10.1016/j.ress.2008.03.014

Cited by 20 publications

(14 citation statements)

References 24 publications

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“…However, the decision-makers' efforts should be focused on minimising the former, 12 in order to reduce the risk of the after-sales service deployment, understanding the risk as the effect of the uncertainty impact on OEMs' and asset users' objectives. 18 Generally, these uncertainties, which are quantified through statistical analysis and confidence or tolerance intervals, 19,20 should be assessed in industrial practice at different levels (see Figure 2). 12 1.…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

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After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

Erguido

Crespo

Castellano

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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After-sales maintenance services can be a very profitable source of incomes for original equipment manufacturers (OEM) due to the increasing interest of assets' users on performance-based contracts. However, when it concerns the product value-adding process, OEM have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services; consequently leading to difficulties in offering them efficiently. Furthermore, both due to the high uncertainty of the assets' behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the provision of after-sales services. With the aim of helping the business and maintenance decision makers at this point, this paper proposes a framework for optimising the incomes of after-sales maintenance services through: 1) implementing advanced multi-objective opportunistic maintenance strategies that sistematically consider the assets' operational context in order to perform preventive maintenance during most favourable conditions, 2) considering the specific OEMs' and users' needs, and 3) assessing both internal and external uncertainties that might condition the after-sales services' success. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.

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Section: Uncertainty Assessmentmentioning

confidence: 99%

“…PM decision is made according to the reliability thresholds SRT ikjt (equation 5) and the available capacity of the own resources. VA of the repaired FMs is updated (equation 3) and the new time to failure TTF hik ð Þis calculated (equation (19)).…”

Section: Lcc and Lp Analysismentioning

confidence: 99%

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

Erguido

Crespo

Castellano

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…More generally, setting the value of G or adopting a different stopping criterion remains a critical issue, especially in the light of the findings of [57], where the authors showed that to reduce the computational times, investing on a large number of generations (i.e., "evolution" [57]) is to be preferred to performing a large number of Monte Carlo runs (i.e., "accuracy"). This issue has not been answered by this work, and will be tackled in future research works.…”

Section: Genetic Algorithm Parameters Search Settingmentioning

confidence: 99%

“…In fact, as pointed out in [17] and in [49], few approaches have been propounded in the literature to effectively tackle such multiobjective optimization problems in the presence of uncertain objective functions. These works consider different frameworks for uncertainty representation: probability distributions in [17], [23], [39], [49], [52], [57] fuzzy sets in [31] and [56], and plausibility and belief functions in [14], [33].…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithms for condition-based maintenance optimization under uncertainty

Compare¹,

Martini

Zio

2015

European Journal of Operational Research

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This paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept.The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant.

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“…In most of the real-life maintenance optimization cases, multi-objective optimization is required to ensure a good fit between the model and the industrial problem. In the last few years academics have recognized this, which makes a combination of simulation (e.g., Monte Carlo simulation) and evolutionary algorithms (Coello 2000;Marseguerra et al 2002;Villanueva et al 2008) a promising multi-objective optimization algorithm used in maintenance optimization. Optimization algorithms used in maintenance optimization applications are listed in the classification framework described in Sect.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Maintenance optimization models and criteria

Horenbeek

Pintelon

Muchiri

2010

Int J Syst Assur Eng Manag

114

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Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.

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Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

Cited by 20 publications

References 24 publications

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

Genetic algorithms for condition-based maintenance optimization under uncertainty

Maintenance optimization models and criteria

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