Genetic programming for job shop scheduling

Su Nguyen, Mengjie Zhang, Mark Johnston, Kay Chen Tan

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer-Verlag
Pages143-167
Number of pages25
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume779
ISSN (Print)1860-949X

Keywords

  • Genetic programming
  • Heuristic
  • Job shop scheduling

ASJC Scopus subject areas

  • Artificial Intelligence

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