Abstract
System design is a difficult process with many design-choices for which the impact may be difficult to foresee. Manufacturing system design is no exception to this. Increased use of flexible manufacturing systems which are able to perform different operations/use-cases further raises the design complexity. One important criterion to consider is the overall makespan and associated critical path for the different use-cases of the system. Stochastic critical path analysis plays a fundamental role in providing useful feedback for system designers to evaluate alternative specifications, which traditional fixed-time analysis cannot. In this paper, we extend our formal model-based framework, for the specification and design of manufacturing systems, with stochastic analysis abilities by associating a criticality index to each action performed by the system. This index can then be visualized and used within the framework such that a system designer can make better informed decisions. We propose a Monte-Carlo method as an estimation algorithm and we explicitly define and use confidence intervals to achieve an acceptable estimation error. We further demonstrate the use of the extended framework and stochastic analysis with an example manufacturing system.
| Original language | English |
|---|---|
| Title of host publication | FDL 2017 - Proceedings of the 2017 Forum on Specification and Design Languages |
| Place of Publication | Piscataway |
| Publisher | IEEE Computer Society |
| Pages | 1-8 |
| Number of pages | 8 |
| Volume | 2017-September |
| ISBN (Electronic) | 978-1-5386-4733-2 |
| ISBN (Print) | 978-1-5386-1152-4 |
| DOIs | |
| Publication status | Published - 27 Feb 2018 |
| Event | 2017 Forum on Specification and Design Languages, FDL 2017 - Verona, Italy Duration: 18 Sep 2017 → 20 Sep 2017 |
Conference
| Conference | 2017 Forum on Specification and Design Languages, FDL 2017 |
|---|---|
| Country | Italy |
| City | Verona |
| Period | 18/09/17 → 20/09/17 |
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Keywords
- Bottleneck identification
- Criticality analysis
- Formal specification
- Manufacturing systems
Cite this
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Identifying bottlenecks in manufacturing systems using stochastic criticality analysis. / Nogueira Bastos, J.P.; van der Sanden, L.J.; Donk, O.; Voeten, J.P.M.; Stuijk, S.; Schiffelers, R.R.H.; Corporaal, H.
FDL 2017 - Proceedings of the 2017 Forum on Specification and Design Languages. Vol. 2017-September Piscataway : IEEE Computer Society, 2018. p. 1-8 8303901.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Identifying bottlenecks in manufacturing systems using stochastic criticality analysis
AU - Nogueira Bastos, J.P.
AU - van der Sanden, L.J.
AU - Donk, O.
AU - Voeten, J.P.M.
AU - Stuijk, S.
AU - Schiffelers, R.R.H.
AU - Corporaal, H.
PY - 2018/2/27
Y1 - 2018/2/27
N2 - System design is a difficult process with many design-choices for which the impact may be difficult to foresee. Manufacturing system design is no exception to this. Increased use of flexible manufacturing systems which are able to perform different operations/use-cases further raises the design complexity. One important criterion to consider is the overall makespan and associated critical path for the different use-cases of the system. Stochastic critical path analysis plays a fundamental role in providing useful feedback for system designers to evaluate alternative specifications, which traditional fixed-time analysis cannot. In this paper, we extend our formal model-based framework, for the specification and design of manufacturing systems, with stochastic analysis abilities by associating a criticality index to each action performed by the system. This index can then be visualized and used within the framework such that a system designer can make better informed decisions. We propose a Monte-Carlo method as an estimation algorithm and we explicitly define and use confidence intervals to achieve an acceptable estimation error. We further demonstrate the use of the extended framework and stochastic analysis with an example manufacturing system.
AB - System design is a difficult process with many design-choices for which the impact may be difficult to foresee. Manufacturing system design is no exception to this. Increased use of flexible manufacturing systems which are able to perform different operations/use-cases further raises the design complexity. One important criterion to consider is the overall makespan and associated critical path for the different use-cases of the system. Stochastic critical path analysis plays a fundamental role in providing useful feedback for system designers to evaluate alternative specifications, which traditional fixed-time analysis cannot. In this paper, we extend our formal model-based framework, for the specification and design of manufacturing systems, with stochastic analysis abilities by associating a criticality index to each action performed by the system. This index can then be visualized and used within the framework such that a system designer can make better informed decisions. We propose a Monte-Carlo method as an estimation algorithm and we explicitly define and use confidence intervals to achieve an acceptable estimation error. We further demonstrate the use of the extended framework and stochastic analysis with an example manufacturing system.
KW - Bottleneck identification
KW - Criticality analysis
KW - Formal specification
KW - Manufacturing systems
UR - http://www.scopus.com/inward/record.url?scp=85045630106&partnerID=8YFLogxK
U2 - 10.1109/FDL.2017.8303901
DO - 10.1109/FDL.2017.8303901
M3 - Conference contribution
AN - SCOPUS:85045630106
SN - 978-1-5386-1152-4
VL - 2017-September
SP - 1
EP - 8
BT - FDL 2017 - Proceedings of the 2017 Forum on Specification and Design Languages
PB - IEEE Computer Society
CY - Piscataway
ER -