Using View-Based Architecture Descriptions to Aid in Automated Runtime Planning for a Smart Factory

Daun, Marian; Brings, Jennifer; Aluko, Patricia; Weiss, Stefanie; Böhm, Birthe; Unverdorben, Stephan

doi:10.1109/icsa-c.2019.00043

Cited by 7 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, an important question arises as to how to achieve the collaboration for optimal decision making in supply network composed of both internal departments and external enterprises. Yet for customization products in smart factory, design is being developed concurrently with manufacturing activities [89], and there are unknown parts of the product structure [90]. Hence, more effort should be applied to the optimized decision-making in this type of production planning.…”

Section: Main Findings and Future Directionsmentioning

confidence: 99%

Review on Smart Factory Operations: A Bibliometric Analysis

Yang

et al. 2022

AMM

View full text Add to dashboard Cite

Over the last few years, existing and emerging Information and Communication Technologies (ICT) and artificial intelligence have been changing the way that factories conduct their manufacturing activities. Operation system of smart factories has been of great interest to researchers in recent years. However, the research concerning operations for the smart factory is still at the nascent stage. To address this need, we conduct a citation and co-citation analysis on smart factory operation system research published in the 11-year period from 2010-2020. A total of 351 papers were selected from Web of Science database. In the citation analysis, we depend on the degree centrality and betweenness centrality to identify 36 important papers. In addition, our main path analysis reveals the role of ICT in facilitating fast development of operation in smart factory. In the co-citation analysis, we identify four major research themes: resource reconfiguration, predictive production planning model, collaborative scheduling mechanism and technology basis of logistics. This is among the first studies to examine the knowledge structure of smart factory operations research by using evidence-based analysis methods. Recommendations for the future research directions have suggested based on our analysis.

show abstract

Section: Main Findings and Future Directionsmentioning

confidence: 99%

Review on Smart Factory Operations: A Bibliometric Analysis

Yang

et al. 2022

AMM

View full text Add to dashboard Cite

show abstract

“…Therefore, the time and costs for each step must be calculated so that the optimal solution can be found. For further information on the views presented and the principles of the matching method, please refer to [Daun et al 2019a].…”

Section: Capability Matching For the Adaptable And Flexible Factorymentioning

confidence: 99%

Modeling and Analyzing Context-Sensitive Changes during Runtime

Wehrstedt

Brings

Caesar

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

Self Cite

View full text Add to dashboard Cite

For collaborative embedded systems, it is essential to consider not only the behavior of each system and the interaction between systems, but also the interaction of systems with their often dynamic and unknown context.In this chapter, we present a solution approach based on process building blocks— describing both the modelling approach as well as the model execution approach—for engineering and operation to achieve the goal of developing systems that deal with dynamics in their open context at runtime by re-using the models from the engineering phase.

show abstract

A GRL-compliant iStar extension for collaborative cyber-physical systems

et al. 2021

View full text Add to dashboard Cite

Collaborative cyber-physical systems are capable of forming networks at runtime to achieve goals that are unachievable for individual systems. They do so by connecting to each other and exchanging information that helps them coordinate their behaviors to achieve shared goals. Their highly complex dependencies, however, are difficult to document using traditional goal modeling approaches. To help developers of collaborative cyber-physical systems leverage the advantages of goal modeling approaches, we developed a GRL-compliant extension to the popular iStar goal modeling language that takes the particularities of collaborative cyber-physical systems and their developers’ needs into account. In particular, our extension provides support for explicitly distinguishing between the goals of the individual collaborative cyber-physical systems and the network and for documenting various dependencies not only among the individual collaborative cyber-physical systems but also between the individual systems and the network. We provide abstract syntax, concrete syntax, and well-formedness rules for the extension. To illustrate the benefits of our extension for goal modeling of collaborative cyber-physical systems, we report on two case studies conducted in different industry domains.

show abstract

Using View-Based Architecture Descriptions to Aid in Automated Runtime Planning for a Smart Factory

Cited by 7 publications

References 24 publications

Review on Smart Factory Operations: A Bibliometric Analysis

Review on Smart Factory Operations: A Bibliometric Analysis

Modeling and Analyzing Context-Sensitive Changes during Runtime

A GRL-compliant iStar extension for collaborative cyber-physical systems

Contact Info

Product

Resources

About