Human factor modelled by fuzzy logic in preventive maintenance actions

Segura, María Soledad; Hennequin, Sophie; Finel, Brigitte

doi:10.1504/ijor.2016.078468

Cited by 6 publications

(1 citation statement)

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“…For a few years, many authors have been interested in the integration of the human factor in production activities with approaches based on mathematical models (Bayesian networks, fuzzy logic, mathematical programming...) because the decisions taken without integrating the human factor do not allow us to reach the obtained results Moreover, the development of digital twins and cyber-physical systems requires the development of such studies. Some of the prominent fields and studies in this context are as the following: mathematical modeling of human factors' impact on production and/or maintenance activities [1][2][3][4], integration of human factors in the manufacturing design process and Industry 4.0 tools [5,6] and multi-agent based systems for ERP [7,8] However, current monitoring methods are insufficient for assessing or enhancing the health status of personnel and play no major influence in an interconnected model with organizational information systems like ERP. In recent years, numerous studies have been conducted to explain the health status of shift-work employees [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

Faghihi

Kazerooni

2023

Scientia Iranica

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Section: Introductionmentioning

confidence: 99%

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

Faghihi

Kazerooni

2023

Scientia Iranica

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Towards Advanced Applications

Witczak,

Seybold,

Bulach

et al. 2023

Studies in Systems, Decision and Control

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The chapter aims at providing a list of selected potential applications of KIS.ME. They stems from the authors’ scientific deliberations and can be perceived as prospective proofs-of-concept, possible to be deployed in various industries. The chapter starts with modelling users and their interactions, which include various important components, e.g., experience and performance. The resulting models make it possible to schedule the work of human operators in a reasonable and predictable way. In particular, two cases are considered, i.e., assembly and transportation tasks. In the former, it is shown how to use these models for integrating user operators in a semi-automated assembly system. In the latter, the concept of human-operated multiple transporters is introduced. Additionally, as most indoor transporters use various kinds of accumulators, it is profitable to take into account their health. Thus, it is shown how to include such information in the scheduling procedure. As a result, the overall accumulator health of a transporter fleet can be balanced. Subsequently, an advanced training exercise is presented, which pertains to work scheduling of three workers operating a conveyor belt. Finally, the chapter is concluded with a concise summary.

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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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Human factor modelled by fuzzy logic in preventive maintenance actions

Cited by 6 publications

References 0 publications

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

Towards Advanced Applications

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

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