IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i3d10.1007_s10845-015-1039-3.html
   My bibliography  Save this article

An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Author

Listed:
  • Maroua Nouiri

    (Faculty of Science of Tunis)

  • Abdelghani Bekrar

    (UVHC, LAMIH Laboratory
    University of Lille Nord de France)

  • Abderezak Jemai

    (Faculty of Science of Tunis)

  • Smail Niar

    (UVHC, LAMIH Laboratory
    University of Lille Nord de France)

  • Ahmed Chiheb Ammari

    (Carthage University
    King Abdulaziz University)

Abstract

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.

Suggested Citation

  • Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:3:d:10.1007_s10845-015-1039-3
    DOI: 10.1007/s10845-015-1039-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1039-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-015-1039-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ying Sun & Jeng-Shyang Pan & Pei Hu & Shu-Chuan Chu, 2023. "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1639-1665, April.
    2. Weian Guo & Lei Zhu & Lei Wang & Qidi Wu & Fanrong Kong, 2019. "An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem," Mathematics, MDPI, vol. 7(5), pages 1-12, May.
    3. Ming Jiang & Haihan Yu & Jiaqing Chen, 2023. "Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    4. Mobin, Mohammadsadegh & Li, Zhaojun & Cheraghi, S. Hossein & Wu, Gongyu, 2019. "An approach for design Verification and Validation planning and optimization for new product reliability improvement," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    5. Jann Michael Weinand & Kenneth Sorensen & Pablo San Segundo & Max Kleinebrahm & Russell McKenna, 2020. "Research trends in combinatorial optimisation," Papers 2012.01294, arXiv.org.
    6. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    7. Zhongwei Zhang & Lihui Wu & Tao Peng & Shun Jia, 2018. "An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    8. Robson Flavio Castro & Moacir Godinho-Filho & Roberto Fernandes Tavares-Neto, 2022. "Dispatching method based on particle swarm optimization for make-to-availability," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1021-1030, April.
    9. Husam Suleiman, 2022. "A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing," Future Internet, MDPI, vol. 14(11), pages 1-21, November.
    10. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    11. Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:29:y:2018:i:3:d:10.1007_s10845-015-1039-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.