A product requirement development method based on multi-layer heterogeneous networks

Xu, Xiangqian; Dou, Yajie; Ouyang, Weijun; Jiang, Jiang; Yang, Kewei; Tan, Yuejin

doi:10.1016/j.aei.2023.102184

Cited by 6 publications

(1 citation statement)

References 70 publications

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“…Finally, ref. [51] used a MSN model for continuous development of product requirements that are subject to frequent changes over time.…”

Section: Related Workmentioning

confidence: 99%

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Wagner

2024

Systems

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Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.

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“…Finally, ref. [51] used a MSN model for continuous development of product requirements that are subject to frequent changes over time.…”

Section: Related Workmentioning

confidence: 99%

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Wagner

2024

Systems

View full text Add to dashboard Cite

show abstract

A product requirement influence analysis method based on multilayer dynamic heterogeneous networks

Xu,

Dou,

Ouyang

et al. 2024

Advanced Engineering Informatics

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CGCI: Cross-granularity Causal Inference framework for engineering Change Propagation Analysis

Wang,

Cai,

Shen

et al. 2024

Advanced Engineering Informatics

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A product requirement development method based on multi-layer heterogeneous networks

Cited by 6 publications

References 70 publications

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

A product requirement influence analysis method based on multilayer dynamic heterogeneous networks

CGCI: Cross-granularity Causal Inference framework for engineering Change Propagation Analysis

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