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Evolutionary and Swarm Intelligence Algorithms pp 143–167Cite as

Genetic Programming for Job Shop Scheduling

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 779))

Abstract

Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.

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Author information

Authors and Affiliations

  1. La Trobe University, Bundoora, Australia

    Su Nguyen

  2. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  3. University of Worcester, Worcester, UK

    Mark Johnston

  4. City University of Hong Kong, Kowloon Tong, Hong Kong

    Kay Chen Tan

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  1. Su Nguyen
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  2. Mengjie Zhang
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  3. Mark Johnston
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  4. Kay Chen Tan
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Corresponding author

Correspondence to Su Nguyen .

Editor information

Editors and Affiliations

  1. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  2. ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  3. Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute, Kolkata, West Bengal, India

    Nikhil R. Pal

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Nguyen, S., Zhang, M., Johnston, M., Tan, K.C. (2019). Genetic Programming for Job Shop Scheduling. In: Bansal, J., Singh, P., Pal, N. (eds) Evolutionary and Swarm Intelligence Algorithms. Studies in Computational Intelligence, vol 779. Springer, Cham. https://doi.org/10.1007/978-3-319-91341-4_8

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