Technology Portfolio Planning by Weighted Graph Analysis of System Architectures

Abstract

Many systems undergo significant architecture‐level change throughout their lifecycles in order to adapt to new operating and funding contexts, to react to failed technology development, or to incorporate new technologies. In all cases early architecture selection and technology investment decisions will constrain the system to certain regions of the tradespace, which can limit the evolvability of the system and its robustness to exogenous changes. In this paper we present a method for charting development pathways within a tradespace of potential architectures, with a view to enabling robustness to technology portfolio realization and later architectural changes. The tradespace is first transformed into a weighted, directed graph of architecture nodes with connectivity determined by relationships between technology portfolios and functional architecture. The tradespace exploration problem is then restated as a shortest path problem through this graph. This method is applied to the tradespace of in‐space transportation architectures for missions to Mars, finding that knowledge of pathways through the tradespace can identify negative coupling between functional architectures and particular technologies, as well as identify ways to prioritize future technology investments.

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

“…e time-expanded decision network (TDN) [23] method aims to select the development path with minimum cost under different operating scenarios. Davison et al [24] also proposed a graph-theory-based method for charting development pathways with a trade space of potential architectures, with a view to enabling robustness to technology portfolio realization and later architectural changes. Tan et al [25] proposed a multiobjective evolutionary algorithm (EA) to obtain system development plans informing which system components should be advanced to which maturity levels within resource limit.…”

Petri net modeling and analysis of an iPad manufacturing system

“…Assume that on each node ( i , j ) ∈ E of G(N; E) , there is a weight value w ij . If a total weight of T(N; E’) is the smallest in all the spanning trees, then T(N;E’) is called the minimal spanning tree (MST) of G(N; E) …”

Defining Better Test Strategies with Tradespace Exploration Techniques and Pareto Fronts: Application in an Industrial Project

“…Tradespace exploration has been proven to support design methods that are effective in resolving ambiguity and facilitating communication, understanding, and agreement between multiple stakeholders [Ross et al, 2004;Golkar and Crawley, 2014]. Furthermore, it also facilitates understanding underlying conflicting objectives during requirement elicitation and conceptual design [Salado and Nilchiani, 2015] and can support the planning of technology portfolios [Davison, Cameron, and Crawley, 2015]. In addition, the capabilities of tradespace exploration are being steadily evolved to incorporate for example uncertainty in the determination of the Pareto front [Mattson and Messac, 2005], change over time in the tradespace configuration and location of the Pareto front Lewis and Mattson, 2012;Curry and Ross, 2015], value robustness [Ross, Rhodes, and Hastings, 2009], or framing and visualization effects for facilitating negotiation [Fitzgerald and Ross, 2014].…”

“…Similar boundary conditions favored the emergence of tradespace exploration techniques and Pareto frontiers for conceptual design [Ross and Hastings, 2005]. Academia has consistently proven the benefits of such techniques over point designs to achieve more affordable solutions [Ross et al, 2004;Ryan, Sarkani and Mazzuchi, 2014;Spero et al, 2014b;Davison, Cameron and Crawley, 2015]. In fact, tradespace exploration is slowly becoming part of standards in industrial contexts as well [Randii et al, 2013].…”