Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments

Der-Chiang Li, Long Sheng Chen, Yao San Lin

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

When a scheduling environment is static and system attributes are deterministic, a manufacturing schedule can be obtained by applying analytical tools such as mathematical modelling technology, dynamic programming, the branch-and-bound method or other developed searching algorithms. Unfortunately, a scheduling environment is usually dynamic in a real manufacturing world. A production system may vary with time and require production managers to change schedule repeatedly. Therefore, the main aim here was to find a scheduling method that could reduce the need for rescheduling. An approach called Functional Virtual Population was proposed as assistance to learn robust scheduling knowledge for manufacturing systems under rationally changing environments. The used techniques include machine learning with artificial neural networks and IF-THEN scheduling rules. To illustrate the study in detail, a simulated flexible manufacturing system consisting of four machines, four parts, one automatic guided vehicle and eight buffers was built as the foundation for learning the concept. Also, Pythia software (a back-propagation-based neural networks) was employed as the learning tool in the learning procedure.

Original languageEnglish
Pages (from-to)4011-4024
Number of pages14
JournalInternational Journal of Production Research
Volume41
Issue number17
DOIs
Publication statusPublished - 2003 Jan 1

All Science Journal Classification (ASJC) codes

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

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