Multiobjective permutation flow shop scheduling using MOEA/D with local search

Yu Teng Chang, Tsung Che Chiang

研究成果: 書貢獻/報告類型會議貢獻

4 引文 斯高帕斯(Scopus)

摘要

This paper addresses the multiobjective permutation flow shop scheduling problem, where makespan and total flow time are to be minimized simultaneously. We solve the problem by an extended version of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). We investigate the effects of scalarization functions and the replacement mechanism. We also incorporate local search into MOEA/D and investigate design issues including individuals to do local search and resource allocation. Experiments are conducted on 90 public problem instances with different scale, and research findings are reported. Comparing with the state of the art, our algorithm shows competitive performance on small-scale instances and superior performance on medium- and large-scale instances.

原文英語
主出版物標題TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面262-269
頁數8
ISBN(電子)9781509057320
DOIs
出版狀態已發佈 - 2017 三月 16
事件2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016 - Hsinchu, 臺灣
持續時間: 2016 十一月 252016 十一月 27

出版系列

名字TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings

其他

其他2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016
國家臺灣
城市Hsinchu
期間16/11/2516/11/27

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Optimization
  • Information Systems

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  • 引用此

    Chang, Y. T., & Chiang, T. C. (2017). Multiobjective permutation flow shop scheduling using MOEA/D with local search. 於 TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings (頁 262-269). [7880168] (TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2016.7880168