Selecting a preventive maintenance scheduling method by using simulation and multi criteria decision making

Eslami, Shirin; Sajadi, Seyed Mojtaba; Kashan, Ali Hosseinzadeh

doi:10.1504/ijlsm.2014.062329

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Maintenance prioritization (Farhan & Fwa, 2009;Moazami et al, 2011;Taghipour et al, 2011) (Ouma et al, 2015) (Wakchaure & Jha, 2011) (Liu et al, 2012;Cafiso et al, 2002;Hankach et al, 2011;Trojan & Morais, 2012b) (Monte & de Almeida-Filho, 2016;de et al, 2015b) Resource Planning (Azadeh et al, 2013) Almeida et al, 2013) (Garmabaki et al, 2016;de Almeida., 2001;Liu & Frangopol, 2006) Maintenance Scheduling (Coulter et al, 2006;Eslami et al, 2014) ( Van et al, 2013) (Certa et al, 2013) (Almeida, A. T., 2012) Operational Level…”

Section: Expert Maintenance Systemsmentioning

confidence: 99%

Data quality assessment of maintenance reporting procedures

Madhikermi

Kubler

Robert

et al. 2016

Expert Systems with Applications

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Today's largest and fastest growing companies' assets are no longer physical, but rather digital (software, algorithms. . . ). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises' daily maintenance data reporting (e.g., maintenance planners, plant managers. . . ), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a Maintenance Reporting Quality Assessment (MRQA) dashboard that enables any company stakeholder to easily -and in real-time -assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices.

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Section: Expert Maintenance Systemsmentioning

confidence: 99%

Data quality assessment of maintenance reporting procedures

Madhikermi

Kubler

Robert

et al. 2016

Expert Systems with Applications

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show abstract

“…Lack of preventive maintenance increases failure rates, which increases maintenance costs (Alimian et al 2020 ). As pointed by Eslami et al ( 2014 ), such decision involves a trade-off between the cost of maintenance execution and the cost avoidance achieved through the activity continuity and overall system failure reduction.…”

Section: Introductionmentioning

confidence: 99%

Straightening machine preventive maintenance intervention plan based on AHP: a case study in a steel company in Brazil

et al. 2023

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Equipment failure and production shortfalls can cause significant impacts on companies. Preventive maintenance can potentially increase equipment reliability and availability, helping companies achieve their corporate goals while reducing operation and maintenance costs. However, determining the optimal interval between maintenance interventions involves balancing various factors such as maintenance costs, equipment availability, risk of failures, and the need for corrective maintenance, as well as potential operational and financial impacts. Under this context, this paper aims to define the interval between preventive maintenance interventions for a key production equipment in a steel company located in Southeast region of Brazil. Upon documental analysis and interviews with experts, an AHP was applied to hierarchize the maintenance periodicity, based on the following criteria: part replacement based on service life, downtime hours for preventive maintenance, equipment reliability, and cost of materials and labour. A biweekly preventive maintenance periodicity was identified as the most adequate for the analysed device. An implementation plan was approved and preliminary results indicate no availability deterioration, while maintenance costs are below historical average.

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“…Moreover, fuzzy logic is used as a suitable strategy for analyzing systems that lack previous records in maintenance (Biswas and Zaman, 2019). And so, these conditions can be decided by forming a fuzzy expert system (Nasruddin et al, 2017) and fuzzy inferences in order to determine the remaining life of such equipment (Hu et al, 2018) or the best maintenance policy (Eslami et al, 2014). Also, the approach is used to determine the RCM strategies (Felecia, 2014), seeming to be applicable in one-shot devices.…”

mentioning

confidence: 99%

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

Azimian

Karbasian

Atashgar

et al. 2021

JQME

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PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

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Selecting a preventive maintenance scheduling method by using simulation and multi criteria decision making

Cited by 8 publications

References 39 publications

Data quality assessment of maintenance reporting procedures

Data quality assessment of maintenance reporting procedures

Straightening machine preventive maintenance intervention plan based on AHP: a case study in a steel company in Brazil

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

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