Distributed learning and control for manufacturing systems scheduling

Joonki Hong, Vittal Prabhu

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

1 Scopus citations

Abstract

A new distributed learning and control (DLC) approach is presented in this paper which integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning control. This approach is suitable for just-in-time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. While part controllers are adjusting their associated parts’ arrival time to minimize due-date deviation, machine controllers equipped with learning components are searching for optimal dispatching policies. The machines’ control problem is modeled as Semi Markov Decision Process (SMDP) and solved using one-step Q-learning. The DLC algorithms are evaluated for minimizing the sum of duedate deviation cost and setup cost using simulation. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.

Original languageEnglish (US)
Title of host publicationEngineering of Intelligent Systems - 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001, Proceedings
EditorsLaszlo Monostori, Jozsef Vancza, Moonis Ali
PublisherSpringer Verlag
Pages582-591
Number of pages10
ISBN (Print)3540422196, 9783540422198
DOIs
StatePublished - Jan 1 2001
Event14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001 - Budapest, Hungary
Duration: Jun 4 2001Jun 7 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2070
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001
CountryHungary
CityBudapest
Period6/4/016/7/01

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Hong, J., & Prabhu, V. (2001). Distributed learning and control for manufacturing systems scheduling. In L. Monostori, J. Vancza, & M. Ali (Eds.), Engineering of Intelligent Systems - 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001, Proceedings (pp. 582-591). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2070). Springer Verlag. https://doi.org/10.1007/3-540-45517-5_65