Research on Optimization Algorithm for MFJSSP

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A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this paper. In the combined algorithm the start position of ants was marked by particles optimum position obtained by particle swarm optimization algorithm. Then the traditional ant colony algorithm was improved and was used to search the global optimum scheduling. The combined algorithm was validated by practical instances. The results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.

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1318-1321

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May 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] . Bruker, R. Schlie, Job-shop scheduling with multi-purpose machines, Computing, (1990), p.369–375.

DOI: 10.1007/bf02238804

Google Scholar

[2] A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by ant colonies in European Conference on Artificial Life, Elsevier Publishing, Amsterdam , (1991), pp.134-142.

Google Scholar

[3] J. Kennedy, R C. Eberhert, Particle swarm optimization, In proceeding of the IEEE International Conference on Neural networks, IV [C]. Piscataway: IEEE Service Center, (1995), p.1942-(1948).

Google Scholar

[4] I. Kacem, S. Hammadi and P. Borne, Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems, IEEE Trans. Syst. Man Cybern, (2002), p.32(1): 1–13.

DOI: 10.1109/tsmcc.2002.1009117

Google Scholar

[5] Stuetzle T., Hoos H H., Max–min ant system, Future Generation Computer System, (2000), p.16: 889–914.

DOI: 10.1016/s0167-739x(00)00043-1

Google Scholar

[6] M. Dorigo, T. Stutzle, Ant colony optimization, MIT Press, Cambridge, MA, (2004).

Google Scholar

[7] W. J. Xia, Z. M. Wu, An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems, Comput. Ind. Eng, (2005), p.409–425.

DOI: 10.1016/j.cie.2005.01.018

Google Scholar