Quantitative Set-Based Design to Inform Design Teams

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD...

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Main Authors: Eric Specking, Nicholas Shallcross, Gregory S. Parnell, Edward Pohl
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/3/1239
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spelling doaj-9db770d03e1d417d8419208770c6662c2021-01-30T00:02:48ZengMDPI AGApplied Sciences2076-34172021-01-01111239123910.3390/app11031239Quantitative Set-Based Design to Inform Design TeamsEric Specking0Nicholas Shallcross1Gregory S. Parnell2Edward Pohl3Department of Industrial Engineering, University of Arkansas, Fayetteville, NC 72701, USADepartment of Industrial Engineering, University of Arkansas, Fayetteville, NC 72701, USADepartment of Industrial Engineering, University of Arkansas, Fayetteville, NC 72701, USADepartment of Industrial Engineering, University of Arkansas, Fayetteville, NC 72701, USASystem designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.https://www.mdpi.com/2076-3417/11/3/1239decision analysistradespace explorationset-based designteam-based methodssystems engineeringmodel-based engineering
collection DOAJ
language English
format Article
sources DOAJ
author Eric Specking
Nicholas Shallcross
Gregory S. Parnell
Edward Pohl
spellingShingle Eric Specking
Nicholas Shallcross
Gregory S. Parnell
Edward Pohl
Quantitative Set-Based Design to Inform Design Teams
Applied Sciences
decision analysis
tradespace exploration
set-based design
team-based methods
systems engineering
model-based engineering
author_facet Eric Specking
Nicholas Shallcross
Gregory S. Parnell
Edward Pohl
author_sort Eric Specking
title Quantitative Set-Based Design to Inform Design Teams
title_short Quantitative Set-Based Design to Inform Design Teams
title_full Quantitative Set-Based Design to Inform Design Teams
title_fullStr Quantitative Set-Based Design to Inform Design Teams
title_full_unstemmed Quantitative Set-Based Design to Inform Design Teams
title_sort quantitative set-based design to inform design teams
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-01-01
description System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.
topic decision analysis
tradespace exploration
set-based design
team-based methods
systems engineering
model-based engineering
url https://www.mdpi.com/2076-3417/11/3/1239
work_keys_str_mv AT ericspecking quantitativesetbaseddesigntoinformdesignteams
AT nicholasshallcross quantitativesetbaseddesigntoinformdesignteams
AT gregorysparnell quantitativesetbaseddesigntoinformdesignteams
AT edwardpohl quantitativesetbaseddesigntoinformdesignteams
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