Modeling of Automated Guided Vehicles in Material Transport within Flexible Manufacturing Systems Using Fuzzy Logic

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Modern automated manufacturing environments are highly agile and are confronted with continuous change in market demands/customers’ requirements. Flexible Manufacturing Systems (FMSs) cater to the need of extensive flexibility and ability to manufacture different variety of batch quantity of components simultaneously in the highly dynamic environment. In FMSs, automated guided vehicle (AGV) systems are commonly used to control complex automated material handling systems. Path design as well as planning and control of AVGs are challenging problems for researchers. In this work, an attempt has been made to model AGVs in FMS using fuzzy logic technique as a modeling tool. A hypothetical FMS system with two AGVs, four processing stations, one buffer and an input/output station is considered. Parts routing, processing operations, allotted machines, processing time and part-mix variation are inputted. AGVs movement to be accomplished for movement of parts obtained is based on rule base consisting of ‘If – then’ statements. Movement of AGVs in FMS without any deadlock for moving the part in an optimized path so as to achieve maximum utilization of the system is aimed. The methodology is demonstrated with illustrative examples.

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77-83

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September 2024

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© 2024 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. Yadav, S.C. Jayswal,. Modelling of flexible manufacturing system: a review. Int. J. Prod. Res. (2018) 56(7), 2464-2487.

Google Scholar

[2] A.M. Abazari, M. Solimanpur, H. Sattari, Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm. Comput Ind Eng. (2012) 62(2), 469-478.

DOI: 10.1016/j.cie.2011.10.013

Google Scholar

[3] N. Buyurgan, L. Meyyappan, C. Saygin, C.H. Dagli, Real‐time routing selection for automated guided vehicles in a flexible manufacturing system. J. Manuf. Technol. Manag. (2007) 18(2), 69-181.

DOI: 10.1108/17410380710722881

Google Scholar

[4] A. Florescu, S.A. Barabas, Modeling and simulation of a flexible manufacturing system—A basic component of industry 4.0. Appl. Sci. (2020). 10(22), 8300.

DOI: 10.3390/app10228300

Google Scholar

[5] M. De Ryck, M. Versteyhe, F. Debrouwere, Automated guided vehicle systems, state-of-the-art control algorithms and techniques. J. Manuf. Syst. (2020) 54, 152-173.

DOI: 10.1016/j.jmsy.2019.12.002

Google Scholar

[6] A. Goli, E.B. Tirkolaee, N.S. Aydın, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors. IEEE transactions on fuzzy systems. (2021) 29(12), 3686-3695.

DOI: 10.1109/tfuzz.2021.3053838

Google Scholar

[7] M. Khairudin, R. Refalda, S. Yatmono, H.S. Pramono, A.K. Triatmaja, A. Shah, The mobile robot control in obstacle avoidance using fuzzy logic controller, Indones. J. Sci. Technol. (2020) 5(3), 334-351.

DOI: 10.17509/ijost.v5i3.24889

Google Scholar

[8] F.T. Chan, A. Kazerooni, K. Abhary, A fuzzy approach to operation selection. Eng. Appl. Artif. Intell. (1997) 10(4), 345-356.

Google Scholar

[9] W.P.N.D. Reis, G.E. Couto, O.M. Junior, Automated guided vehicles position control: a systematic literature review. J. Intell. Manuf.. (2023) 34(4), 1483-1545.

DOI: 10.1007/s10845-021-01893-x

Google Scholar

[10] M. Singh, S. Das, S.K. Mishra, Static obstacles avoidance in autonomous ground vehicle using fuzzy logic controller. IEEE International Conference for Emerging Technology (INCET), (2020) 1-6.

DOI: 10.1109/incet49848.2020.9154145

Google Scholar