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Intelligent and Evolutionary Systems pp 195–210Cite as

Dynamic Job Shop Scheduling Under Uncertainty Using Genetic Programming

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

Job shop scheduling (JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop scheduling under uncertainty. We consider uncertainty in processing times and consider multiple job types pertaining to different levels of uncertainty. In particular, we propose an approach to use exponential moving average of the deviations of the processing times in the dispatching rules. We test the performance of the proposed approach under different uncertain scenarios. Our results show that the proposed method performs significantly better for a wide range of uncertain scenarios.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Deepak Karunakaran, Yi Mei, Gang Chen & Mengjie Zhang

Authors
  1. Deepak Karunakaran
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  2. Yi Mei
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  3. Gang Chen
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Deepak Karunakaran .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

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Karunakaran, D., Mei, Y., Chen, G., Zhang, M. (2017). Dynamic Job Shop Scheduling Under Uncertainty Using Genetic Programming. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

  • eBook Packages: EngineeringEngineering (R0)

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