Genetic Programming for Job Shop Scheduling.pdf (1.35 MB)

Genetic programming for job shop scheduling

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posted on 29.10.2020, 00:45 by S Nguyen, Mengjie Zhang, M Johnston, KC Tan
© 2019, Springer International Publishing AG, part of Springer Nature. 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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Preferred citation

Nguyen, S., Zhang, M., Johnston, M. & Tan, K. C. (2019). Genetic programming for job shop scheduling. Studies in Computational Intelligence (779, pp. 143-167). Springer International Publishing. https://doi.org/10.1007/978-3-319-91341-4_8

Book title

Studies in Computational Intelligence

Publisher

Springer International Publishing

Pagination

143-167

Volume

779

ISBN

9783319913391

ISSN

1860-949X

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