An integrated Petri net and GA based approach for scheduling of hybrid plants

Authors:
S. A. Sadrieh;M. Ghaeli;P. A. Bahri;P. L. Lee
Affiliations:
Department of Computer Engineering, IUST University, Narmak, Tehran 16844, Iran;Department of Chemical Engineering, Curtin University, Bentley, WA 6845, Australia;School of Electrical, Energy and Process Engineering, Murdoch University, Dixon Road, Rockingham, WA 6150, Australia;Chancellery, University of South Australia, Currie Street, SA 5000, Australia
Venue:
Computers in Industry
Year:
2007

Citing 6
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Genetic algorithms and job shop scheduling

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A genetic algorithm for the job shop problem

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Evolution based learning in a job shop scheduling environment

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Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
On Hybrid Petri Nets

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An analysis of the behavior of a class of genetic adaptive systems.

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A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

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Abstract

Production scheduling in mixed batch/continuous plants, due to their hybrid nature, can become very complex. The main contribution of this paper is the presentation of a novel approach based on the integration of the Timed arc hybrid Petri net (TAHPN) and a genetic algorithm (GA) as a suitable tool for scheduling of hybrid systems. The major benefit of this approach is a significant reduction in complexity during problem formulation. The proposed method is explained through a sugar mill case study.