Sequencing the Reconfigurable Assembly Line with a Hybrid Multi-Objective Genetic Algorithm

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Abstract:

In order to solve the reconfigurable assembly line sequencing problem, a multi-objective optimization mathematical model is presented, which includes three practically important objectives. Such as minimizing the total utility work cost, minimizing the total production rate variation and minimizing reconfigurable setup cost are considered. A scheduling method for reconfigurable assembly line is proposed based on Pareto multi-objective genetic algorithm, In order to ensure the group’s variety, prevent the premature convergence problem and enhance the globe-optimization capability, some key technologies such as population ranking method, Niche technique are applied. The adaptive crossover and mutation probabilities methods are developed. The computational results show that the proposed hybrid algorithm finds solutions with better quality especially in the case of large-sized problems.

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Periodical:

Advanced Materials Research (Volumes 160-162)

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1545-1550

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Online since:

November 2010

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] McMullen, P.R., Frazier, G.V. A simulated annealing approach to mixed-model sequencing with multiple objectives on a JIT line. IIE Transactions, 2000, 32 (8), 679–686.

DOI: 10.1080/07408170008967426

Google Scholar

[2] Kim S, Jeong B Product sequencing problem in mixed modelassembly line to minimize unfinished works. Compute Indust Eng (in press).

Google Scholar

[3] Rahimi-Vahed A, Mirzaei AH. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Comput Ind Eng, 2007, 53: 642–666.

DOI: 10.1016/j.cie.2007.06.007

Google Scholar

[4] Guo ZX, Wong WK, Leung SYS, Fan JT. Intelligent production control decision support system for flexible assembly lines. Expert Syst Appl 2009, 36: 4268–4277.

DOI: 10.1016/j.eswa.2008.03.023

Google Scholar

[5] Chen WC, Hsu YY, Hsieh LF, Tai PH . A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN. Expert Syst Appl , 2010, 37: 716–726.

DOI: 10.1016/j.eswa.2009.05.098

Google Scholar

[6] C.J. Hyun, Y. Kim, Y.K. Kim, A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Computers and Operations Research, 1998, 25 : 675~690.

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

Google Scholar

[7] Miltenburg J. Level schedules for mixed-model assembly lines in just-in-time production systems. Manage Sci, 1998, 35(2): 192–207.

DOI: 10.1287/mnsc.35.2.192

Google Scholar

[8] MondenY. Toyota production system. Institute if industrial engineers Press. Atlanta, GA, (1983).

Google Scholar