<div class="csl-bib-body">
<div class="csl-entry">Meixner, K., Feichtinger, K., Fadhlillah, H. S., Greiner, S., Marcher, H., Rabiser, R., & Biffl, S. (2024). Variability modeling of products, processes, and resources in cyber–physical production systems engineering. <i>Journal of Systems and Software</i>, <i>211</i>, Article 112007. https://doi.org/10.1016/j.jss.2024.112007</div>
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dc.identifier.issn
0164-1212
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/200042
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dc.description.abstract
Cyber-Physical Production Systems (CPPSs), such as automated car manufacturing plants, execute a configurable sequence of production steps to manufacture products from a product portfolio. In CPPS engineering, domain experts start with manually determining feasible production step sequences and resources based on implicit knowledge. This process is hard to reproduce and highly inefficient. In this paper, we present the Extended Iterative Process Sequence Exploration (eIPSE) approach to derive variability models for products, processes, and resources from a domain-specific description. To automate the integrated exploration and configuration process for a CPPS, we provide a toolchain which automatically reduces the configuration space and allows to generate CPPS artifacts, such as control code for resources. We evaluate the approach with four real-world use cases, including the generation of control code artifacts, and an observational user study to collect feedback from engineers with different backgrounds. The results confirm the usefulness of the eIPSE approach and accompanying prototype to straightforwardly configure a desired CPPS.
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dc.description.sponsorship
Christian Doppler Forschungsgesells; Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.publisher
ELSEVIER SCIENCE INC
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dc.relation.ispartof
Journal of Systems and Software
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dc.subject
Cyber–physical production system
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dc.subject
Decision modeling
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dc.subject
Feature modeling
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dc.subject
Production process variability
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dc.subject
Variability modeling
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dc.title
Variability modeling of products, processes, and resources in cyber–physical production systems engineering