Parallel genetic algorithm for a flow-shop problem with multiprocessor tasks

C. Oguz, Yu Fai Fung, M. Fikret Ercan, X. T. Qi

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

5 Citations (Scopus)

Abstract

Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Since most scheduling problems are NP-hard, it is impossible to find the optimal schedule in reasonable time. In this paper, we consider a flow-shop scheduling problem with multiprocessor tasks. A parallel genetic algorithm using multithreaded programming technique is developed to obtain a quick but good solution to the problem. The performance of the parallel genetic algorithm under various conditions and parameters are studied and presented.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsPeter M.A. Sloot, David Abramson, Alexander V. Bogdanov, Yuriy E. Gorbachev, Jack J. Dongarra, Albert Y. Zomaya
PublisherSpringer Verlag
Pages548-559
Number of pages12
ISBN (Print)9783540401964
DOIs
Publication statusPublished - Jan 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2659
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Genetic algorithms
  • Parallel architectures
  • Parallel computing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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