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Creating a Multi-iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming

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Learning and Intelligent Optimization (LION 12 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

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Abstract

Genetic programming is used to create Priority Rules (PR) for the Job Shop Scheduling Problem with the aim of minimizing the weighted sum of tardy jobs. Four types of structures are used for the PR: Normal PR, Iterative-PR (IPR), Multi-PR (MPR), and Multi-Iterative-PR (MIPR). These are then compared among one another and with classical PR like shortest-processing-time. A modern metaheuristic based on local search using disjunct graphs and critical paths is used to solve the static problem as a benchmark. The results show that all types provide better results than classical PR and that with and without time limit the types from best to worst are: MIPR, MPR, IPR, and PR. The gaps to the metaheuristic are also reported.

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Authors and Affiliations

  1. Faculty of Business Administration, University of Vienna, 1090, Vienna, Austria

    Georg E. A. Froehlich, Guenter Kiechle & Karl F. Doerner

  2. Data Science, University of Vienna, Vienna, Austria

    Karl F. Doerner

Authors
  1. Georg E. A. Froehlich
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  2. Guenter Kiechle
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  3. Karl F. Doerner
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Corresponding author

Correspondence to Georg E. A. Froehlich .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Froehlich, G.E.A., Kiechle, G., Doerner, K.F. (2019). Creating a Multi-iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

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