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A Fuzzy Genetic Algorithm for Real-World Job Shop Scheduling

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Innovations in Applied Artificial Intelligence (IEA/AIE 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3533))

Abstract

In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the number of tardy jobs. Fuzzy sets are used to represent satisfaction grades for the objectives taking into consideration the preferences of the decision maker. A genetic algorithm is developed to search for the solution with maximum satisfaction grades for the objectives. The developed algorithm is tested on real-world data from a printing company. The experiments include different aggregation operators for combining the objectives.

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

    Carole Fayad & Sanja Petrovic

Authors
  1. Carole Fayad
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  2. Sanja Petrovic
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

  2. Dipartimento di Informatica, Università degli Studi di Bari,  

    Floriana Esposito

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© 2005 Springer-Verlag Berlin Heidelberg

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Fayad, C., Petrovic, S. (2005). A Fuzzy Genetic Algorithm for Real-World Job Shop Scheduling. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_71

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  • DOI: https://doi.org/10.1007/11504894_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26551-1

  • Online ISBN: 978-3-540-31893-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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