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A New Class of Greedy Heuristics for Job Shop Scheduling Problems

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Experimental and Efficient Algorithms (WEA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2647))

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Abstract

In this paper we introduce a new class of greedy heuristics for general job shop scheduling problems. In particular we deal with the classical job shop, i.e. with unlimited capacity buffer, and job shop problems with blocking and no-wait. The proposed algorithm family is a simple randomized greedy family based on a general formulation of the job shop problem. We report on an extensive study of the proposed algorithms, and comparisons with other greedy algorithms are presented.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica e Automazione, Università degli Studi di Roma Tre, Via della Vasca Navale 79, 00146, Roma, Italy

    Marco Pranzo & Dario Pacciarelli

  2. Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via E. Orabona 4, 70125, Bari, Italy

    Carlo Meloni

Authors
  1. Marco Pranzo
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  2. Carlo Meloni
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  3. Dario Pacciarelli
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Applied Mathematics, University of Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Klaus Jansen  & Marian Margraf  & 

  2. IDISIA - Instituto Dalle Molle di Studi sull’Intelligenza Artificiale, Galleria 2, 6928, Manno, Switzerland

    Monaldo Mastrolilli

  3. Université de Genève 4, Switzerland

    José D. P. Rolim

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© 2003 Springer-Verlag Berlin Heidelberg

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Pranzo, M., Meloni, C., Pacciarelli, D. (2003). A New Class of Greedy Heuristics for Job Shop Scheduling Problems. In: Jansen, K., Margraf, M., Mastrolilli, M., Rolim, J.D.P. (eds) Experimental and Efficient Algorithms. WEA 2003. Lecture Notes in Computer Science, vol 2647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44867-5_19

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  • DOI: https://doi.org/10.1007/3-540-44867-5_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40205-3

  • Online ISBN: 978-3-540-44867-9

  • eBook Packages: Springer Book Archive

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