Integration of inductive learning and neural networks for multi-objective FMS scheduling

Chang Ouk Kim, H. S. Min, Y. Yih

Research output: Contribution to journalArticle

57 Citations (Scopus)

Abstract

In this paper, we propose an integrated approach of inductive learning and competitive neural networks for developing multi-objective flexible manufacturing system (FMS) schedulers. Simulation and competitive neural networks are applied sequentially to extract a set of classified training data which is used to create a compact set of scheduling rules through inductive learning. The FMS scheduler can assist the operator to make decisions in real time, while satisfying multiple objectives desired by the operator. A simulation-based experiment is performed to evaluate the performance of the resulting scheduler.

Original languageEnglish
Pages (from-to)2497-2509
Number of pages13
JournalInternational Journal of Production Research
Volume36
Issue number9
DOIs
Publication statusPublished - 1998 Jan 1

Fingerprint

Flexible manufacturing systems
Scheduling
Neural networks
Experiments
Simulation
Operator
Learning networks
Inductive learning
Multiple objectives
Integrated approach
Scheduling rules
Experiment

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

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Integration of inductive learning and neural networks for multi-objective FMS scheduling. / Kim, Chang Ouk; Min, H. S.; Yih, Y.

In: International Journal of Production Research, Vol. 36, No. 9, 01.01.1998, p. 2497-2509.

Research output: Contribution to journalArticle

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