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October  2016, 12(4): 1391-1415. doi: 10.3934/jimo.2016.12.1391

A priority-based genetic algorithm for a flexible job shop scheduling problem

1. 

Department of Industrial Engineering, İstanbul Technical University, 34367 İstanbul, Turkey, Turkey

2. 

ALGORITMI Research Centre, University of Minho, Campus Azurem, 4800-058 Guimarães, Portugal

3. 

Center for Applied Optimization Department of Industrial and Systems Engineering, University of Florida, 32611

Received  January 2015 Revised  June 2015 Published  January 2016

In this study, a genetic algorithm (GA) with priority-based representation is proposed for a flexible job shop scheduling problem (FJSP) which is one of the hardest operations research problems. Investigating the effect of the proposed representation schema on FJSP is the main contribution to the literature. The priority of each operation is represented by a gene on the chromosome which is used by a constructive algorithm performed for decoding. All active schedules, which constitute a subset of feasible schedules including the optimal, can be generated by the constructive algorithm. To obtain improved solutions, iterated local search (ILS) is applied to the chromosomes at the end of each reproduction process. The most widely used FJSP data sets generated in the literature are used for benchmarking and evaluating the performance of the proposed GA methodology. The computational results show that the proposed GA performed at the same level or better with respect to the makespan for some data sets when compared to the results from the literature.
Citation: Didem Cinar, José António Oliveira, Y. Ilker Topcu, Panos M. Pardalos. A priority-based genetic algorithm for a flexible job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1391-1415. doi: 10.3934/jimo.2016.12.1391
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show all references

References:
[1]

Flexible Services and Manufacturing Journal, 23 (2011), 64-85. doi: 10.1007/s10696-010-9067-y.  Google Scholar

[2]

International Journal of Production Research, 48 (2010), 5671-5689. doi: 10.1080/00207540903055743.  Google Scholar

[3]

in Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on, 1 (2009), 505-510. doi: 10.1109/HIS.2009.104.  Google Scholar

[4]

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[6]

Journal of Intelligent Manufacturing, 15 (2004), 777-785. doi: 10.1023/B:JIMS.0000042663.16199.84.  Google Scholar

[7]

in Handbook of Applied Optimization (eds. P. M. Pardalos and M. G. Resende), Oxford University Press, 2002. doi: 10.1007/978-1-4757-5362-2.  Google Scholar

[8]

Technical report, Helmut-Schmidt-University, Logistics Management Department, Hamburg, Germany, 2012. Google Scholar

[9]

Computers and Industrial Engineering, 59 (2010), 323-333. doi: 10.1016/j.cie.2010.05.004.  Google Scholar

[10]

Annals of Operations Research, 41 (1993), 157-183. Google Scholar

[11]

Computing, 45 (1990), 369-375. doi: 10.1007/BF02238804.  Google Scholar

[12]

Discrete Applied Mathematics, 156 (2008), 201-217. doi: 10.1016/j.dam.2006.07.013.  Google Scholar

[13]

J. B. Chambers and J. W. Barnes, Flexible job shop scheduling by tabu search,, 1996., ().   Google Scholar

[14]

International Journal of Production Research, 28 (1990), 65-74. doi: 10.1080/00207549008942684.  Google Scholar

[15]

in Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on, 2 (1999), 1120-1125. doi: 10.1109/ROBOT.1999.772512.  Google Scholar

[16]

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[17]

International Journal of Production Economics, 113 (2008), 626-640. doi: 10.1016/j.ijpe.2007.10.015.  Google Scholar

[18]

in Applications of Evolutionary Computing (ed. E. Boers), vol. 2037 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2001, 441-451. doi: 10.1007/3-540-45365-2_46.  Google Scholar

[19]

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[20]

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[21]

Computers & Operations Research, 35 (2008), 2599-2616. doi: 10.1016/j.cor.2006.12.019.  Google Scholar

[22]

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[23]

Journal of Intelligent Manufacturing, 18 (2007), 331-342. doi: 10.1007/s10845-007-0026-8.  Google Scholar

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[27]

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[28]

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[29]

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[30]

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[31]

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[32]

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[33]

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[34]

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[38]

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[39]

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[42]

Mathematics and Computers in Simulation, 60 (2002), 245-276. doi: 10.1016/S0378-4754(02)00019-8.  Google Scholar

[43]

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[59]

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[64]

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[65]

Computers and Operations Research, 35 (2008), 3202-3212. doi: 10.1016/j.cor.2007.02.014.  Google Scholar

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The International Journal of Advanced Manufacturing Technology, 64 (2013), 915-932. doi: 10.1007/s00170-012-4051-1.  Google Scholar

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[83]

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Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 37 (2007), 652-661. doi: 10.1109/TSMCC.2007.897494.  Google Scholar

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