Integrating inductive learning and simulation in rule-based scheduling

Sang Chan Park; Selwyn Piramuthu; Narayan Raman; Michael J. Shaw

doi:10.1007/3-540-53104-1_39

Integrating inductive learning and simulation in rule-based scheduling

Sang Chan Park, Selwyn Piramuthu, Narayan Raman, Michael J. Shaw

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.

Original language	English
Title of host publication	Expert Systems in Engineering
Subtitle of host publication	Principles and Applications - International Workshop, Proceedings
Editors	Georg Gottlob, Wolfgang Nejdl
Publisher	Springer Verlag
Pages	152-167
Number of pages	16
ISBN (Print)	9783540467113
DOIs	https://doi.org/10.1007/3-540-53104-1_39
Publication status	Published - 1990
Event	International Workshop on Expert Systems in Engineering, 1990 - Vienna, Austria Duration: 24 Sept 1990 → 26 Sept 1990

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	462 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Expert Systems in Engineering, 1990
Country/Territory	Austria
City	Vienna
Period	24/09/90 → 26/09/90

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1990.

Access to Document

10.1007/3-540-53104-1_39

Cite this

Park, S. C., Piramuthu, S., Raman, N., & Shaw, M. J. (1990). Integrating inductive learning and simulation in rule-based scheduling. In G. Gottlob, & W. Nejdl (Eds.), Expert Systems in Engineering: Principles and Applications - International Workshop, Proceedings (pp. 152-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 462 LNAI). Springer Verlag. https://doi.org/10.1007/3-540-53104-1_39

Park, Sang Chan ; Piramuthu, Selwyn ; Raman, Narayan et al. / Integrating inductive learning and simulation in rule-based scheduling. Expert Systems in Engineering: Principles and Applications - International Workshop, Proceedings. editor / Georg Gottlob ; Wolfgang Nejdl. Springer Verlag, 1990. pp. 152-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Integrating inductive learning and simulation in rule-based scheduling",

abstract = "This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.",

author = "Park, {Sang Chan} and Selwyn Piramuthu and Narayan Raman and Shaw, {Michael J.}",

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year = "1990",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Park, SC, Piramuthu, S, Raman, N & Shaw, MJ 1990, Integrating inductive learning and simulation in rule-based scheduling. in G Gottlob & W Nejdl (eds), Expert Systems in Engineering: Principles and Applications - International Workshop, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 462 LNAI, Springer Verlag, pp. 152-167, International Workshop on Expert Systems in Engineering, 1990, Vienna, Austria, 24/09/90. https://doi.org/10.1007/3-540-53104-1_39

Integrating inductive learning and simulation in rule-based scheduling. / Park, Sang Chan; Piramuthu, Selwyn; Raman, Narayan et al.
Expert Systems in Engineering: Principles and Applications - International Workshop, Proceedings. ed. / Georg Gottlob; Wolfgang Nejdl. Springer Verlag, 1990. p. 152-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 462 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Integrating inductive learning and simulation in rule-based scheduling

AU - Park, Sang Chan

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AU - Shaw, Michael J.

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PY - 1990

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N2 - This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.

AB - This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.

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U2 - 10.1007/3-540-53104-1_39

DO - 10.1007/3-540-53104-1_39

M3 - Conference contribution

AN - SCOPUS:33747619404

SN - 9783540467113

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 152

EP - 167

BT - Expert Systems in Engineering

A2 - Gottlob, Georg

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PB - Springer Verlag

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Park SC, Piramuthu S, Raman N, Shaw MJ. Integrating inductive learning and simulation in rule-based scheduling. In Gottlob G, Nejdl W, editors, Expert Systems in Engineering: Principles and Applications - International Workshop, Proceedings. Springer Verlag. 1990. p. 152-167. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-53104-1_39

Integrating inductive learning and simulation in rule-based scheduling

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