A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

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Abstract:

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

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Periodical:

Advanced Materials Research (Volumes 121-122)

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266-270

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June 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Mastrolilli and L. M. Gambardella: Effective �eighborhood Functions for the Flexible Job Shop Problem, Journal of Scheduling, vol. 1(2002), pp.3-20.

DOI: 10.1002/(sici)1099-1425(200001/02)3:1<3::aid-jos32>3.0.co;2-y

Google Scholar

[2] I. Kacem, S. Hammadi and P. Borne: Pareto-optimality Approach for Flexible Job Shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic, Mathematics and Computers in Simulation(2002), pp.245-276.

DOI: 10.1016/s0378-4754(02)00019-8

Google Scholar

[3] D. Dasgupta and F. González: Artificial Immune Systems (AIS) Research in the Last Five Years, in Proc. Conf. Evolutionary Computation(2003), pp.123-130.

Google Scholar

[4] W. Xia and Z. Wu: An Effective Hybrid Optimization Approach for Multi-objective Flexible Job-shop Scheduling Problems, Computers and Industrial Engineering(2005), pp.409-425.

DOI: 10.1016/j.cie.2005.01.018

Google Scholar

[5] L. De Castro, J. Fernando and Von Zuben: Learning and Optimization Using Clonal Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems(2001), pp.239-251.

DOI: 10.1109/tevc.2002.1011539

Google Scholar

[6] M. Vrahatis, G. Androulakis and M. Manoussakis: A �ew Unconstrained Optimization Method for Imprecise Function and Gradient Values, Journal of Mathematical Analysis and Applications(1996), pp.586-607.

DOI: 10.1006/jmaa.1996.0041

Google Scholar