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Genetic Programming for Job Shop Scheduling

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Nguyen, S., Zhang, M., Johnston, Mark and Tan, K.C. (2019) Genetic Programming for Job Shop Scheduling. In: Evolutionary and Swarm Intelligence Algorithms. Studies in Computational Intelligence, 779 . Springer International Publishing, Cham, pp. 143-167. ISBN Print: 978-3-319-91339-1 Online: 978-3-319-91341-4

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Official URL: https://link.springer.com/chapter/10.1007/978-3-31...

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.

Item Type:	Book Section
Additional Information:	The full-text cannot be supplied for this item. Please check availability with your local library or Interlibrary Requests service.
Uncontrolled Discrete Keywords:	genetic programming, job shop scheduling, heuristics, dispatching rules, computational intelligence
Subjects:	T Technology > T Technology (General)
Divisions:	College of Health, Life and Environmental Sciences > School of Science and the Environment
Related URLs:	https://link.springer.com/book/10.1007/9... https://link.springer.com/
Depositing User:	Mark Johnston
Date Deposited:	01 Mar 2019 10:57
Last Modified:	17 Jun 2020 17:27
URI:	https://eprints.worc.ac.uk/id/eprint/7579

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