Name
Abstract | ||
|---|---|---|
Maintaining coherence between system functional, performance, production and operational requirements is a key to the ability to optimize the design of large-scale systems. Different architectural configurations entail significant differences in functionality, performance, ease of manufacturing/assembly and operational behavior. While the first two are the usual concerns in architectural tradeoff analysis, the last two, reflected by manufacturability and operational metrics, such as manufacturability and affordability, are often neglected in architectural optimization. In this work, we propose a methodology to derive the formal specification of operational metrics applicable to design optimization based on life cycle processes, such as “manufacturing”, “sunny day operation”, and “unplanned maintenance”. These operational metrics are presented in the context of an industrial case study for an Unmanned Underwater Vehicle (UUV) to provide context for the recommended approach. We suggest an approach to (1) define libraries of reusable operational metrics based on architectural properties, (2) build reusable data processing patterns to calculate these architectural properties, and (3) map calculated architectural parameters to a specific design model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.procs.2014.03.059 | Procedia Computer Science |
Keywords | Field | DocType |
Design Space Exploration,Operational Metrics,Operational Data,Black Box Integration,Reusable Data Patterns | Data mining,Data processing,Systems engineering,Computer science,Simulation,Coherence (physics),Formal specification,Operational requirements,Architectural pattern,Design for manufacturability,Design space exploration,Unmanned underwater vehicle | Conference |
Volume | ISSN | Citations |
28 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Masin | 1 | 121 | 13.55 |
| Henry Broodney | 2 | 2 | 1.14 |
| Candace Brown | 3 | 0 | 0.34 |
| Lior Limonad | 4 | 58 | 7.12 |
| Nir Mashkif | 5 | 6 | 2.23 |
| Aviad Sela | 6 | 37 | 5.65 |