Research on Bi-Objective Scheduling of Dual-Resource Constrained Flexible Job Shop

Xiao Xia Liu; Chun Bo Liu; Ze Tao

doi:10.4028/www.scientific.net/AMR.211-212.1091

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 211-212Research on Bi-Objective Scheduling of...

Research on Bi-Objective Scheduling of Dual-Resource Constrained Flexible Job Shop

Abstract:

A hybrid genetic algorithm based on Pareto was proposed and applied to the flexible job shop scheduling problem (FJSP) with bi-objective, and the bi-objective FJSP optimization model was built, where the make-span and the production cost were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method, and the niche technology and four kinds of crossover operations are used in order to promote solution diversity. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. In this paper, the influence of the proportion of workers and machines on the scheduling result is researched on the basis of the hybrid genetic algorithm and the result is in accord with other researchers. In conclusion, the algorithm proposed in this paper is available and efficient.

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Periodical:

Advanced Materials Research (Volumes 211-212)

Pages:

1091-1095

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.211-212.1091

Citation:

Cite this paper

Online since:

February 2011

Authors:

Xiao Xia Liu, Chun Bo Liu, Ze Tao

Keywords:

Bi-Objective, Dual Resources, Hybrid Genetic Algorithm, Job Shop Scheduling

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References

[1] L. Wang: Job Shop Scheduling and Genetic Algorithms. Beijing: Tsinghua University Press, (2002), in Chinese.

Google Scholar

[2] W. Wei, J. R. Tan, Y. X. Feng: Multi-objective optimization method research on flexible job shop scheduling problem. Computer Integrated Manufacturing Systems, vol. 15(2009), pp.1592-1598.

Google Scholar

[3] Z. J. Sun, J. Y. Zhu: Intelligent optimization for job shop scheduling of dual-resources. Journal of southeast university (Natural Science Edition), vol. 35(2005), pp.376-381, in Chinese.

Google Scholar

[4] X. X. Liu, L. Y. Xie, Z. Tao: Flexible Job Shop Scheduling for Decreasing Production Costs. Journal of Northeastern University (Natural Science), vol. 29(2008), pp.561-564, in Chinese.

Google Scholar

[5] Z. J. Sun, J. Y. Zhu: Intelligent optimization for job shop scheduling of dual-resources, Journal of southeast university (Natural Science Edition), vol. 35(2005), pp.376-381, in Chinese.

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[6] X. X. Liu: Multi-objective JSP Scheduling Based on OCTPN and Hybrid Genetic Algorithms. Shenyang: Doctor dissertation of Northeastern University, (2008).

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[7] Byung J, Hyung R, Hyun S: A hybrid genetic algorithm for the job shop scheduling problems. Computers & Industrial Engineering, vol. 45(2003), pp.597-613.

DOI: 10.1016/s0360-8352(03)00077-9

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