A Dynamic Simulation Approach for Flexible Manufacturing System Design

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Achieving the greatest flexibility is the key objective for a manufacturing enterprise to design and install a Flexible Manufacturing System (FMS). Unfortunately, before the contents of “flexibility” is explicitly defined and commonly accepted within the company, the design effectiveness of an FMS will never be formally justified; not to mention evaluating its production performance once the FMS is implemented. The objective of this paper is twofold: first it presents a practical and quantitative measure of performance for an FMS by introducing the Machine Flexibility (MF) and the subsequent System Flexibility (SF). The second objective of this paper is then to develop a generic architecture for optimally designing an FMS which considers not just manufacturing and economic constraints but also dynamic perturbations from the shop floor. Machine flexibility comprises two parts: 1) the descriptive segment provides the operation type information and 2) the quantitative segment uses a weighted relative scaling (WRS) method to evaluate the flexibility based on machine power generation, operation cycle time, machine design mechanics, working volume, machining precision, and controller performance. System flexibility contains five attributes for an FMS: power generation, system design mechanics, working volume, system precision, and dynamic performance. The adaptive architecture for designing an FMS is composed of three modules: the design preprocessor, the reference system generator, and the alternative systems generator..

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1813-1816

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September 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] B. Huang, R. Jiang, G. Zhang, Search strategy for scheduling flexible manufacturing systems simultaneously using admissible heuristic functions and nonadmissible heuristic functions, Computers & Industrial Engineering, Vol. 71 (2014), pp.21-26.

DOI: 10.1016/j.cie.2014.02.010

Google Scholar

[2] J.M. Novas, G.P. Henning, Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming, Expert Systems with Applications, Vol. 41, Iss. 5(2014), pp.2286-2299.

DOI: 10.1016/j.eswa.2013.09.026

Google Scholar

[3] A.M. El-Tamimi, M.H. Abidi, S.H. Mian, J. Aalam, Analysis of performance measures of flexible manufacturing system, Journal of King Saud University - Engineering Sciences, Vol. 24, Iss. 2 (2012), pp.115-129.

DOI: 10.1016/j.jksues.2011.06.005

Google Scholar

[4] Y.F. Chen, Z.W. Li, Design of a maximally permissive liveness-enforcing supervisor with a compressed supervisory structure for flexible manufacturing systems, Automatica, Vol. 47, Iss. 5(2011), pp.1028-1034.

DOI: 10.1016/j.automatica.2011.01.070

Google Scholar

[5] I. Um, H. Cheon, H. Lee, The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System, Journal of Manufacturing Systems, Vol. 28, Iss. 4 (2009), pp.115-122.

DOI: 10.1016/j.jmsy.2010.06.001

Google Scholar

[6] M.C. Ruiz, D. Cazorla, F. Cuartero, H. Macia, Improving performance in flexible manufacturing systems", The Journal of Logic and Algebraic Programming, Vol. 78, Iss. 4 (2009), pp.260-273.

DOI: 10.1016/j.jlap.2008.11.002

Google Scholar

[7] Y.P. Gupta, S. Goyal, Flexibility of manufacturing systems: Concepts and measurements, European Journal of Operational Research, Vol. 43 (1989), pp.119-135.

DOI: 10.1016/0377-2217(89)90206-3

Google Scholar