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A Hybrid Particle Swarm Optimization Method for Permutation Flow Shop Scheduling Problem

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Human Centered Computing (HCC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8944))

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Abstract

The Permutation Flow Shop Scheduling Problem (PFSP) is a typical example in Production Scheduling, which has attracted many researchers’ attention. This paper takes to the advantage of the swarm characteristic of Particle Swarm optimization (PSO) algorithm to find the best particle in the solution space. The objective is to minimize the makespan. Firstly, the initial solution of the algorithm is generated by the famous heuristic NEH algorithm. The NEH algorithm was used to initialize the particle of global extreme values. Secondly, we take some optimized strategy to set the parameters, acceleration constant and nonlinear inertia weight strategy which based on random self-adaptively by means of chaos method for setting parameters. These optimized methods can avoid algorithm to be trapped in local optimum. At last, simulated results demonstrate that the hybrid PSO method is feasible and effective for the PFSP.

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong Normal University, No. 88 East of Wenhua Road , Jinan, 250014, Shandong, China

    Lin Wang, Jianhua Qu & Yuyan Zheng

Authors
  1. Lin Wang
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  2. Jianhua Qu
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  3. Yuyan Zheng
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Corresponding author

Correspondence to Jianhua Qu .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe, Middlesex, United Kingdom

    Bo Hu

  3. Fudan University, Shanghai, China

    Ning Gu

  4. Telecommunications and Information Communication Technology, National Institute of Posts, Phnom Penh, Cambodia

    Sopheap Seng

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Wang, L., Qu, J., Zheng, Y. (2015). A Hybrid Particle Swarm Optimization Method for Permutation Flow Shop Scheduling Problem. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-15554-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15553-1

  • Online ISBN: 978-3-319-15554-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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