A unique fuzzy multivariate modeling approach for performance optimization of maintenance workshops with cognitive factors

This paper puts forward an integrated fuzzy simulation-fuzzy data envelopment analysis (FDEA)-fuzzy cognitive map (FCM) algorithm for performance optimization of maintenance workshops by incorporating cognitive and time-dependent factors. Due to severe ambiguousness associated with the collected dat...

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Vydáno v:International journal of advanced manufacturing technology Ročník 90; číslo 1-4; s. 499 - 525
Hlavní autoři: Azadeh, A., Ghaderi, S. F., Pashapour, S., Keramati, A., Malek, M. Rezaei, Esmizadeh, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.04.2017
Springer Nature B.V
Témata:
Algorithms
CAE) and Design
Cognitive maps
Computer simulation
Computer-Aided Engineering (CAD
Data collection
Data envelopment analysis
Engineering
Fuzzy set theory
Fuzzy sets
Industrial and Production Engineering
Maintenance
Mechanical Engineering
Media Management
Modelling
Operations research
Optimization
Sensitivity analysis
Time dependence
Workshops
Fuzzy simulation
Cognitive factors
Fuzzy data envelopment analysis (FDEA)
Maintenance workshop
Fuzzy cognitive map (FCM)
Possibilistic programming
ISSN:0268-3768, 1433-3015
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Shrnutí:This paper puts forward an integrated fuzzy simulation-fuzzy data envelopment analysis (FDEA)-fuzzy cognitive map (FCM) algorithm for performance optimization of maintenance workshops by incorporating cognitive and time-dependent factors. Due to severe ambiguousness associated with the collected data, the fuzzy set theory is incorporated into the approach. Fuzzy computer simulation is employed for modeling the workshop and providing time-dependent factors. FCM is used for extracting relations between cognitive factors. FDEA is used for ranking scenarios based on inputs and outputs of FCM and the developed computer simulation. Moreover, a recent possibilistic programming approach is used to convert the fuzzy DEA model to an equivalent crisp model. The sensitivity analysis show that α  = 0.1 is the best α -cut level for interpreting data. The proposed algorithm is capable of modeling and optimizing the performance of maintenance workshops in uncertain and non-linear environments. The solution quality is inspected and shown through an actual maintenance workshop. This is the first study that presents an integrated non-crisp algorithm for performance optimization of maintenance activities with cognitive and time-dependent factors.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-016-9208-x