Abstract
Today’s more complicated engineering systems make it difficult to analyze system performance accurately due to the increase in the number of components and interconnections within the system. System performance and measures such as failure rate, repair rate and availability are important measures to analyze a system and obtain system productivity. While application of system availability is most widely used for electrical and electronic systems, in recent years it has been started to be used as a performance measure of manufacturing systems. In this study, a novel approach is proposed using both simulation modeling technique and fuzzy availability analysis considering failure and repair times of components to investigate system productivity in a more consistent and logical manner. Simulation model is also used to analyze system behavior and estimate system throughput. Because of insufficient and inaccurate historical data related with component failure and repair times of the considered system, failure and repair data are defined with the fuzzy membership function. This approach provides more detailed information about system characteristics. Thus, more realistic system productivity considering failure and repair times which represents the real behavior of system can be obtained using this approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Verma AK, Srividya A, Prabhu Gaonkar RS, Rajesh S (2007) Fuzzy-reliability engineering: concepts and applications. Narosa
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Lai YJ, Hwang CL (1992) Fuzzy mathematical programming, vol 394. Springer, Heidelberg
Dengiz B, Akbay KS (2000) Computer simulation of a PCB production line: metamodeling approach. Int J Prod Econ 63:195–205
Dengiz B, Bektas T, Ultanir AE (2006) Simulation optimization based DSS application: a diamond tool production line in industry. Simul Model Pract Theory 14:296–312
Padhi SS, Wagner SM, Niranjan TT, Aggarwal V (2013) A simulation-based methodology to analyse production line disruptions. Int J Prod Res 51:1885–1897
Liu A, Yang Y, Liang X, Zhu M, Yao H (2010) Dynamic reentrant scheduling simulation for assembly and test production line in semiconductor industry. Adv Mater Res 97–101:2418–2422
Wazed MA, Ahmed S, Nukman Y (2010) Application of Taguchi method to analyze the impacts of commonalities in multistage production under bottleneck and uncertainty. Int J Phys Sci 5:1576–1591
Kampa A, Gołda G, Paprocka I (2017) Discrete event simulation method as a tool for improvement of manufacturing systems. Computers 6:1–10
Loganathan MK, Kumar G, Gandhi OP (2016) Availability evaluation of manufacturing systems using Semi-Markov model. Int J Comput Integr Manuf 29:720–735
Gupta P, Lal AK, Sharma RK, Singh J (2007) Analysis of reliability and availability of serial processes of plastic pipe manufacturing plant: a case study. Int J Qual Reliab Manag 24:404–419
Görkemli L, Ulusoy SK (2010) Fuzzy Bayesian reliability and availability analysis of production systems. Comput Ind Eng 59:690–696
Knezevic J, Odoom ER (2001) Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology. Reliab Eng Syst Saf 73:1–17
Komal (2018) Fuzzy reliability analysis of DFSMC system in LNG carriers for components with different membership function. Ocean Eng 155:278–294
Elsayed EA (1996) Reliability engineering, vol 1. Addison Wesley Longman
Ke JC, Huang HI, Lin CH (2006) Fuzzy analysis for steady-state availability: a mathematical programming approach. Eng Optim 38:909–921
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dengiz, B., Sahin, M.U., Atalay, K.D. (2019). Investigation of System Productivity with Fuzzy Availability Analysis Considering Failure and Repair Times. In: Durakbasa, N., Gencyilmaz, M. (eds) Proceedings of the International Symposium for Production Research 2018. ISPR 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-92267-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-92267-6_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92266-9
Online ISBN: 978-3-319-92267-6
eBook Packages: EngineeringEngineering (R0)