A Hybrid Population-Based Incremental Learning Algorithm for M-Machine Reentrant Permutation Flow-Shop Scheduling

Zuo Cheng Li; Bin Qian; Rong Hu; Xiao Hong Zhu

doi:10.4028/www.scientific.net/AMR.655-657.1636

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 655-657A Hybrid Population-Based Incremental Learning...

A Hybrid Population-Based Incremental Learning Algorithm for M-Machine Reentrant Permutation Flow-Shop Scheduling

Abstract:

In this paper, a hybrid population-based incremental learning algorithm (HPBIL) is proposed for solving the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP). The objective function is to minimize the maximum completion time (i.e., makespan). In HPBIL, the PBIL with a proposed Insert-based mutation is used to perform global exploration, and an Interchange-based neighborhood search with first move strategy is designed to enhance the local exploitation ability. Computational experiments and comparisons demonstrate the effectiveness of the proposed HPBIL.

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Periodical:

Advanced Materials Research (Volumes 655-657)

Pages:

1636-1641

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.655-657.1636

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Online since:

January 2013

Authors:

Zuo Cheng Li, Bin Qian, Rong Hu, Xiao Hong Zhu

Keywords:

Insert-Based Mutation, Interchange-Based Neighborhood Search, Make Span, M-Machine Reentrant Permutation Flow-Shop Scheduling, Population-Based Incremental Learning Algorithm

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References

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