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CLC number: TP301.6; U11; F406.2

On-line Access: 2010-12-09

Received: 2010-10-28

Revision Accepted: 2010-10-29

Crosschecked: 2010-10-29

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.12 P.953-958

http://doi.org/10.1631/jzus.A1001136


Solving composite scheduling problems using the hybrid genetic algorithm


Author(s):  Azuma Okamoto, Mitsumasa Sugawara

Affiliation(s):  Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate, Japan

Corresponding email(s):   lfo@iwate-pu.ac.jp, sugawara@iwate-pu.ac.jp

Key Words:  Composite scheduling, Manufacturing scheduling, Transportation routing, Hybrid genetic algorithm


Azuma Okamoto, Mitsumasa Sugawara. Solving composite scheduling problems using the hybrid genetic algorithm[J]. Journal of Zhejiang University Science A, 2010, 11(12): 953-958.

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author="Azuma Okamoto, Mitsumasa Sugawara",
journal="Journal of Zhejiang University Science A",
volume="11",
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pages="953-958",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1001136"
}

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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1001136

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T1 - Solving composite scheduling problems using the hybrid genetic algorithm
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J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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Abstract: 
This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems. Two scheduling models were formulated as the elements of the composite scheduling model, and the composite model was formulated composing these models with indispensable additional constraints. A hybrid genetic algorithm was developed to solve the composite scheduling problems. An improved representation based on random keys was developed to search permutation space. A genetic algorithm based dynamic programming approach was applied to select resource. The proposed technique and a previous technique are compared by three types of problems. All results indicate that the proposed technique is superior to the previous one.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bean, J.C., 1994. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6:154-160.

[2]Lee, C.Y., Chen, Z.L., 2001. Machine scheduling with transportation considerations. Journal of Scheduling, 4(1):3-24.

[3]Moon, C., Kim, J.S., Gen, M., 2004. Advanced planning and scheduling based on precedence and resource constraints for e-plant chains. International Journal of Production Research, 42(15):2941-2955.

[4]Okamoto, A., Gen, M., Sugawara, M., 2006a. Integrated data structure and scheduling approach for manufacturing and transportation using hybrid multistage operation-based genetic algorithm. Journal of Intelligent Manufacturing, 17(4):411-421.

[5]Okamoto, A., Gen, M., Sugawara, M., 2006b. Integrated scheduling problem of manufacturing and transportation with pickup and delivery. International Journal of Logistics and SCM Systems, 1(1):19-27.

[6]Okamoto, A., Gen, M., Sugawara, M., 2009. Integrated scheduling using genetic algorithm with quasi-random sequences. International Journal of Manufacturing Technology and Management, 16(1/2):147-165.

[7]Soukhal, A., Oulamara, A., Martineau, P., 2005. Complexity of flow shop scheduling problems with transportation constraints. European Journal of Operational Research, 161(1):32-41.

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