A neural network for dispatching rule selection in a job shop

Ahmed El-Bouri, Pramit Shah

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

This paper investigates an intelligent system that selects dispatching rules to apply locally for each machine in a job shop. Randomly generated problems are scheduled using optimal permutations of three different dispatching rules on five machines. A neural network is then trained to associate between a statistical characterization of the job mix in each of these problems, with the best combination of dispatching rules to use. Once trained, the neural network is able to recommend for new problems a dispatching rule to use on each machine. Two networks are trained separately for minimizing makespan and the mean flowtime in the job shop. Test results show that the combinations of dispatching rules suggested by the trained networks produce better results, for both objectives, than the alternative of using a single rule common to all machines.

Original languageEnglish
Pages (from-to)342-349
Number of pages8
JournalInternational Journal of Advanced Manufacturing Technology
Volume31
Issue number3-4
DOIs
Publication statusPublished - Nov 2006

Keywords

  • Dispatching rules
  • Job shop
  • Makespan
  • Mean flowtime
  • Neural networks

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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