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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2017: Advances in Artificial Intelligence: From Theory to Practice pp 112–117Cite as

Elitist Ant System for the Distributed Job Shop Scheduling Problem

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10350))

Abstract

In this paper, we are interested in industrial plants geographically distributed and more precisely the Distributed Job shop Scheduling Problem (DJSP) in multi-factory environment. The problem consists of finding an effective way to assign jobs to factories then, to generate a good operation schedule. To do this, a bio-inspired algorithm is applied, namely the Elitist Ant System (EAS) aiming to minimize the makespan. Several numerical experiments are conducted to evaluate the performance of our algorithm applied to the Distributed Job shop Scheduling Problem and the results show the shortcoming of the Elitist Ant System compared to developed algorithms in the literature.

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Author information

Authors and Affiliations

  1. COSMOS Laboratory, Université de la Manouba, Manouba, Tunisia

    Imen Chaouch, Olfa Belkahla Driss & Khaled Ghedira

  2. Ecole Nationale des Sciences de l’Informatique, Université de la Manouba, Manouba, Tunisia

    Imen Chaouch

  3. Ecole Supérieure de Commerce de Tunis, Université de la Manouba, Manouba, Tunisia

    Olfa Belkahla Driss

  4. Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunis, Tunisia

    Khaled Ghedira

Authors
  1. Imen Chaouch
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  2. Olfa Belkahla Driss
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  3. Khaled Ghedira
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Corresponding author

Correspondence to Imen Chaouch .

Editor information

Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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© 2017 Springer International Publishing AG

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Chaouch, I., Driss, O.B., Ghedira, K. (2017). Elitist Ant System for the Distributed Job Shop Scheduling Problem. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-60042-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60041-3

  • Online ISBN: 978-3-319-60042-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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