Surrogate-Assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules

Su Nguyen, Mengjie Zhang, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

98 Citations (Scopus)

Abstract

Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.

Original languageEnglish
Article number7473913
Pages (from-to)2951-2965
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume47
Issue number9
DOIs
Publication statusPublished - Sept 2017
Externally publishedYes

Keywords

  • Evolutionary design
  • genetic programming (GP)
  • hyper-heuristic
  • scheduling

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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