Details
| Original language | English |
|---|---|
| Pages (from-to) | 30-53 |
| Number of pages | 24 |
| Journal | International Journal of Enterprise Information Systems (IJEIS) |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - Jan 2006 |
Abstract
Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies.
Keywords
- knowledge and information management, model optimization, performance modeling, Petri Nets
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Computer Science(all)
- Computer Science Applications
- Decision Sciences(all)
- Information Systems and Management
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In: International Journal of Enterprise Information Systems (IJEIS), Vol. 2, No. 1, 01.2006, p. 30-53.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Tool Support for Performance Modeling and Optimization
AU - Syrjakow, Michael
AU - Syrjakow, Elisabeth
AU - Szczerbicki, Helena
N1 - We want to thank Prof. D. Schmid for his encouragement and support of our work. We also thank our students, especially S. Schillinger, F. Schmidt, H. Renfranz, T. Sommer, D. Haag, C. Bentz, J. Gramlich, and A. Kehl, for their engagement and contributions.
PY - 2006/1
Y1 - 2006/1
N2 - Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies.
AB - Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies.
KW - knowledge and information management
KW - model optimization
KW - performance modeling
KW - Petri Nets
UR - http://www.scopus.com/inward/record.url?scp=85001839729&partnerID=8YFLogxK
U2 - 10.4018/jeis.2006010103
DO - 10.4018/jeis.2006010103
M3 - Article
AN - SCOPUS:85001839729
VL - 2
SP - 30
EP - 53
JO - International Journal of Enterprise Information Systems (IJEIS)
JF - International Journal of Enterprise Information Systems (IJEIS)
SN - 1548-1115
IS - 1
ER -