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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 110-116A New Fuzzy Approach for Minimizing Conveyer...

A New Fuzzy Approach for Minimizing Conveyer Stoppages in Mixed-Model Assembly Lines

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Abstract:

Mixed model production is the practice of assembling different and distinct models in a line without changeovers with responding to sudden demand changes for a variety of models. In this paper, we specify sequence of models to minimize the conveyer stoppages. We assume our lines are fixed and we can not change the balance of the lines. When the condition of lines like setup cost, demand of each models change, it is important to specify the sequence for minimizing the conveyer stoppages without balancing the line again, because the main lines are fixed. We consider three objective functions, simultaneously: minimizing the variation in actual and required production capacity of the line, minimizing the objectives which increase the chance of conveyer stoppage including: a) minimizing the total setup time, b) minimizing the total production variation cost, and minimizing the total utility work cost. We use a new fuzzy programming for this problem; it can present us an estimator for nearness of conveyer stoppages. We study about sub lines and conveyer velocity and its affect on stoppages.

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Mechanical and Aerospace Engineering, ICMAE2011

Info:

Periodical:

Applied Mechanics and Materials (Volumes 110-116)

Pages:

4085-4090

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.110-116.4085

Citation:

Cite this paper

Online since:

October 2011

Authors:

Farzad Radmehr, Neda Manavizadeh, Hamed Rafiei, Masoud Rabbani

Keywords:

Conveyer Stoppages, Fuzzy Goal Programming, Mixed-Model Assembly Line, Sub-Lines

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Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] J. Miltenburg, G. Stenier And S. Yeomans, A dynamic programming algorithm for scheduling mixed model, just in time production system, Mathematics and Computer Modelling, vol. 13, no. 3, 1990, pp.57-66.

DOI: 10.1016/0895-7177(90)90370-3

[2] S.M.J. Mirzapour Al-e-Hashem, And M.B. Aryanezhad, An efficient method to solve a mixed-model assembly line sequencing problem considering a sub-line, World Applied Sciences Journal, vol. 6, no. 2, 2009, pp.168-181.

[3] Z. Xiaobo, Z. Zhou, And A. Asres, A note on Toyota's goal of sequencing mixed models on an assembly line, Computers & Industrial Engineering, vol. 36, 1999, pp.57-65.

DOI: 10.1016/s0360-8352(98)00113-2

[4] B. Javadi, A. Rahimi-Vahed, M. Rabbani, And M. Dangchi, Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach, International Journal of Advanced Manufacturing Technology, vol. 39, 2008, pp.975-982.

DOI: 10.1007/s00170-007-1267-6

[5] J. Miltenburg And G. Sinnamon, Scheduling mixed-model multi-level just-in-time production systems, International Journal of Production Research, vol. 27, 1989, pp.487-509.

DOI: 10.1080/00207548908942636

[6] C.J. Hyun, Y. Kim And Y.K. Kim A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines, Computers & Operations Research, vol. 25, no. 7-8, pp.675-690.

DOI: 10.1016/s0305-0548(98)00026-4

[7] S.A. Mansouri, A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines, European Journal of Operational Research, vol. 167, 2005, pp.696-716.

DOI: 10.1016/j.ejor.2004.07.016

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