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Performance Evaluation of Particles Coding in Particle Swarm Optimization with Self-adaptive Parameters for Flexible Job Shop Scheduling Problem

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Recent Trends and Future Technology in Applied Intelligence (IEA/AIE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10868))

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Abstract

The metaheuristic Particle Swarm Optimization (PSO) is well suited to solve the Flexible Job Shop Scheduling Problem (FJSP), and a suitable particle representation should importantly impact the optimization results and performance of this algorithm. The chosen representation has a direct impact on the dimension and content of the solution space. In this paper, we intend to evaluate and compare the performance of two different variants of PSO with different particle representations (PSO with Job-Machine coding Scheme (PSO-JMS) and PSO with Only-Machine coding Scheme (PSO-OMS)) for solving FJSP. These procedures have been tested on thirteen benchmark problems, where the objective function is to minimize the makespan and total workload and to compare the run time of the different PSO variants. Based on the experimental results, it is clear that PSO-OMS gives the best performance in solving all benchmark problems.

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Author information

Authors and Affiliations

  1. Polytechnic School, University of Carthage, 2078, La Marsa, Tunisia

    Rim Zarrouk

  2. LR-NOCCS, National Engineering School of Sousse, University of Sousse, 4023, Sousse, Tunisia

    Rim Zarrouk

  3. Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia

    Abderrazak Jemai

Authors
  1. Rim Zarrouk
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  2. Abderrazak Jemai
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Correspondence to Rim Zarrouk .

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Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Malek Mouhoub

  2. University of Regina, Regina, SK, Canada

    Samira Sadaoui

  3. Concordia University, Montreal, QC, Canada

    Otmane Ait Mohamed

  4. Texas State University, San Marcos, TX, USA

    Moonis Ali

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Zarrouk, R., Jemai, A. (2018). Performance Evaluation of Particles Coding in Particle Swarm Optimization with Self-adaptive Parameters for Flexible Job Shop Scheduling Problem. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_38

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  • DOI: https://doi.org/10.1007/978-3-319-92058-0_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92057-3

  • Online ISBN: 978-3-319-92058-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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