Order-based genetic algorithm for flow shop scheduling

  • Liang Zhang*
  • , Ling Wang
  • , Fang Tang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Flow shop scheduling is one of the most well-known production scheduling problems and a typical NP-hard combinatorial optimization problem with strong engineering background. This paper presents an order-based genetic algorithm for flow shop scheduling, which borrows the idea of ordinal optimization to reduce computation and ensure the quality of the solution found and enforces the evolutionary searching mechanism and learning capability of genetic algorithm. With the guidance of ordinal comparison and by emphasizing the order-based search and elitist-based evolution in the proposed approach, good enough solution can be guaranteed with high confidence level and reduced computation quantity, which is demonstrated by numerical simulation based on some benchmarks. Moreover, some parameter sensitivities are presented and discussed.

Original languageEnglish
Title of host publicationProceedings of 2002 International Conference on Machine Learning and Cybernetics
Pages139-144
Number of pages6
StatePublished - 2002
EventProceedings of 2002 International Conference on Machine Learning and Cybernetics - Beijing, China
Duration: 4 Nov 20025 Nov 2002

Publication series

NameProceedings of 2002 International Conference on Machine Learning and Cybernetics
Volume1

Conference

ConferenceProceedings of 2002 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityBeijing
Period4/11/025/11/02

Keywords

  • Flow shop scheduling
  • Genetic algorithm
  • Ordinal optimization
  • Parameter sensitivity

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