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Optimization of manufacturing processes by distributed simulation.

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Title: Optimization of manufacturing processes by distributed simulation.
Authors: Weigert, G.1 (AUTHOR) weigert@avt.et.tu-dresden.de, Horn, S.2 (AUTHOR), Werner, S.2 (AUTHOR)
Source: International Journal of Production Research. 9/15/2006, Vol. 44 Issue 18/19, p3677-3692. 15p. 6 Diagrams, 2 Charts, 7 Graphs.
Subjects: Manufacturing processes, Management science, Simulation methods & models, Information technology, Optimization & Computational Logic (Book), Multicasting (Computer networks), Production management (Manufacturing), Electronic industries, Semiconductor industry
Abstract: The paper describes a simulation-based optimization system for manufacturing processes. The principle suitability of simple Threshold Accepting and Great Deluge Algorithms for optimizing practical manufacturing systems is shown, as well as the possibility of parallelizing of these algorithms. A special IP Multicast architecture is introduced which allows the simulation model to be extended to multiple clients and so the efficiency of the optimization algorithms to be increased by concurrent simulation. Each client can log on or log out at an optimization server at any time so that it should be possible to use the idle computer capacity of a single production department or a whole company. The optimization server is implemented as an experimental option in the simulation system simcron MODELLER®, which is particularly used in electronics and semiconductor manufacturing. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Optimization of manufacturing processes by distributed simulation.
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  Data: <searchLink fieldCode="AR" term="%22Weigert%2C+G%2E%22">Weigert, G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> weigert@avt.et.tu-dresden.de</i><br /><searchLink fieldCode="AR" term="%22Horn%2C+S%2E%22">Horn, S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Werner%2C+S%2E%22">Werner, S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. 9/15/2006, Vol. 44 Issue 18/19, p3677-3692. 15p. 6 Diagrams, 2 Charts, 7 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Management+science%22">Management science</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Information+technology%22">Information technology</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+%26+Computational+Logic+%28Book%29%22">Optimization & Computational Logic (Book)</searchLink><br /><searchLink fieldCode="DE" term="%22Multicasting+%28Computer+networks%29%22">Multicasting (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Production+management+%28Manufacturing%29%22">Production management (Manufacturing)</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+industries%22">Electronic industries</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+industry%22">Semiconductor industry</searchLink>
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  Data: The paper describes a simulation-based optimization system for manufacturing processes. The principle suitability of simple Threshold Accepting and Great Deluge Algorithms for optimizing practical manufacturing systems is shown, as well as the possibility of parallelizing of these algorithms. A special IP Multicast architecture is introduced which allows the simulation model to be extended to multiple clients and so the efficiency of the optimization algorithms to be increased by concurrent simulation. Each client can log on or log out at an optimization server at any time so that it should be possible to use the idle computer capacity of a single production department or a whole company. The optimization server is implemented as an experimental option in the simulation system simcron MODELLER®, which is particularly used in electronics and semiconductor manufacturing. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207540600851774
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      – Code: eng
        Text: English
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      – SubjectFull: Manufacturing processes
        Type: general
      – SubjectFull: Management science
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      – SubjectFull: Simulation methods & models
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      – SubjectFull: Information technology
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      – SubjectFull: Optimization & Computational Logic (Book)
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      – SubjectFull: Multicasting (Computer networks)
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      – SubjectFull: Production management (Manufacturing)
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      – SubjectFull: Electronic industries
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      – SubjectFull: Semiconductor industry
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      – TitleFull: Optimization of manufacturing processes by distributed simulation.
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              Text: 9/15/2006
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