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Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness

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Advances in Artificial Intelligence (Canadian AI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2671))

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Abstract

The multi-processor total tardiness problem (MPTTP) is an NP-hard scheduling problem, in which the the goal is to minimise the tardness of a set of jobs that are processed on a number of processors. Exact algorithms like branch and bound have proven to be impractical for the MPTTP, leaving stochastic local search (SLS) algorithms as the main alternative to find high-quality schedules. Among the available SLS techniques, iterated local search (ILS)has been shown to be an effective algorithm for the single processor case. Therefore, here we extend this technique to the multi-processor case, but our computational results indicate that ILS performance is not fully satisfying. To enhance ILS performance, we consider the use of population-based ILS extensions. Our final experimental results show that the usage of a population of search trajectories yields a more robust algorithm capable of finding best known solutions to difficult instances more reliably and in less computation time than a single ILS trajectory.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, Canada

    Michael Pavlin & Holger Hoos

  2. Department of Computer Science, Darmstadt University of Technology, Germany

    Thomas Stützle

Authors
  1. Michael Pavlin
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  2. Holger Hoos
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  3. Thomas Stützle
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Editor information

Editors and Affiliations

  1. Department of Computing and Information Science, College of Physical and Engineering Science, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Yang Xiang

  2. Dépt. Informatique-Génie Logiciel, Université Laval, Pavillon Pouliot, Ste-Foy, PQ, Canada, G1K 7P4

    Brahim Chaib-draa

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© 2003 Springer-Verlag Berlin Heidelberg

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Pavlin, M., Hoos, H., Stützle, T. (2003). Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_10

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  • DOI: https://doi.org/10.1007/3-540-44886-1_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40300-5

  • Online ISBN: 978-3-540-44886-0

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