Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781467379298 |
| Publication status | Published - 19 Oct 2015 |
| Event | 20th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2015 - Luxembourg, Luxembourg Duration: 8 Sept 2015 → 11 Sept 2015 |
Publication series
| Name | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA |
|---|---|
| Volume | 2015-October |
| ISSN (Print) | 1946-0740 |
| ISSN (electronic) | 1946-0759 |
Abstract
Planning in a multi-site, non-mass production environment is a special challenge because of several sources of uncertainty. Unlike in mass production facilities, in our setting the current state at all sites cannot be determined easily and exactly due to the spatial distribution of sites and the low degree of automation. For re-planning in case of failures, the possible alternative actions have to be formalized on the decision making facility, where the possible alternatives will then be determined and evaluated. In this work, we will present the necessary components for an automated evaluation of alternatives and decision support procedure. The main challenges are the formalization of product plans including alternative steps and the non-automated collection or assessment of the distributed system state of all sites. In our experiments we evaluate different state update intervals and the effect on prediction accuracy. It turns out, that even sparse updates show significant improvement on the production time in comparison to only local static decisions.
Keywords
- Companies, Decision making, Decision support systems, Maintenance engineering, Planning, Predictive models, Production
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
- Computer Science(all)
- Computer Science Applications
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Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7301545 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Vol. 2015-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On the influence of state update interval length on the prediction success of decision support system in multi-site production environment
AU - Becker, Matthias
AU - Szczerbicka, Helena
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/10/19
Y1 - 2015/10/19
N2 - Planning in a multi-site, non-mass production environment is a special challenge because of several sources of uncertainty. Unlike in mass production facilities, in our setting the current state at all sites cannot be determined easily and exactly due to the spatial distribution of sites and the low degree of automation. For re-planning in case of failures, the possible alternative actions have to be formalized on the decision making facility, where the possible alternatives will then be determined and evaluated. In this work, we will present the necessary components for an automated evaluation of alternatives and decision support procedure. The main challenges are the formalization of product plans including alternative steps and the non-automated collection or assessment of the distributed system state of all sites. In our experiments we evaluate different state update intervals and the effect on prediction accuracy. It turns out, that even sparse updates show significant improvement on the production time in comparison to only local static decisions.
AB - Planning in a multi-site, non-mass production environment is a special challenge because of several sources of uncertainty. Unlike in mass production facilities, in our setting the current state at all sites cannot be determined easily and exactly due to the spatial distribution of sites and the low degree of automation. For re-planning in case of failures, the possible alternative actions have to be formalized on the decision making facility, where the possible alternatives will then be determined and evaluated. In this work, we will present the necessary components for an automated evaluation of alternatives and decision support procedure. The main challenges are the formalization of product plans including alternative steps and the non-automated collection or assessment of the distributed system state of all sites. In our experiments we evaluate different state update intervals and the effect on prediction accuracy. It turns out, that even sparse updates show significant improvement on the production time in comparison to only local static decisions.
KW - Companies
KW - Decision making
KW - Decision support systems
KW - Maintenance engineering
KW - Planning
KW - Predictive models
KW - Production
UR - http://www.scopus.com/inward/record.url?scp=84952879027&partnerID=8YFLogxK
U2 - 10.1109/ETFA.2015.7301545
DO - 10.1109/ETFA.2015.7301545
M3 - Conference contribution
AN - SCOPUS:84952879027
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
BT - Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2015
Y2 - 8 September 2015 through 11 September 2015
ER -