Equal size lot streaming to job-shop scheduling problem using genetic algorithms

F. T. S. Chan, T. C. Wong, L. Y. Chan

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

18 Citations (Scopus)

Abstract

A novel approach to solve Equal Size Lot Streaming (ESLS) in Job-shop Scheduling Problem (JSP) using Genetic Algorithms (GAs) is proposed. LS refers to a situation that a lot can be split into a number of smaller lots (or sub-lots) so that successive operation can be overlapped. By adopting the proposed approach, the sub-lot number for different lots and the processing sequence of all sub-lots can be determined simultaneously using GAs. Applying Just-In-Time (JIT) policy, the results show that the solution can minimize both the overall penalty cost and total setup time with the development of multi-objective function. In this connection, decision makers can then assign various weightings so as to enhance the reliability of the final solution. 

Original languageEnglish
Title of host publicationProceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004
Place of PublicationNew York
PublisherIEEE
Pages472-476
Number of pages5
ISBN (Print)0780386353
DOIs
Publication statusPublished - 1 Dec 2004

Keywords

  • lot sizing
  • genetic algorithms
  • job shop scheduling
  • just-in-time
  • lot streaming

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  • Cite this

    Chan, F. T. S., Wong, T. C., & Chan, L. Y. (2004). Equal size lot streaming to job-shop scheduling problem using genetic algorithms. In Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004 (pp. 472-476). IEEE. https://doi.org/10.1109/ISIC.2004.1387729