Combining Attributes for Systems of Systems in Multi‐Attribute Tradespace Exploration

Abstract

One of the principal value propositions for the creation of Systems of Systems (SoS) is the ability to generate stakeholder value beyond that which can be delivered by a single system or even a collection of systems. The dynamic interactions among the component systems in a SoS make conceptual design decisions for SoS more complex compared to traditional system design. There are several heuristics and qualitative guidelines for designing SoS in the literature, but there is a lack of practical quantitative methods for SoS concept exploration. Development of quantitative methods for SoS conceptual design will greatly improve the ability of decision makers to select SoS designs in the concept design phase that will be value robust over time, by allowing them to consider a larger and more complete set of possible alternative SoS designs than is possible with qualitative methods alone. Multi-Attribute Tradespace Exploration has been used in the past to compare large numbers of system alternatives on a common cost-utility basis. In this method, the designer elicits the decision maker's needs and formulates these as quantified attributes. The systems are then analyzed in terms of their ability to achieve the desired levels of attribute metrics. SoS-level attribute calculations must reflect component system interactions and emergent SoS-level value, as well as the added costs and benefits in the SoS as compared to that of the component systems operating alone. This paper introduces techniques for the SoS attribute combination modeling within the Multi-Attribute Tradespace Exploration method when considering SoS with heterogeneous component systems. Combining the attributes for the SoS-level must take into account the nature of the attributes provided by the component systems as well as the concept of operations for SoS. The techniques used for SoS attribute combination are classified according to the level of coordination between the component systems in the SoS. Using the information about the complexity of the method required along with knowledge about the control structure in the SoS, the SoS architect can estimate the added SoS integration costs. The ability to combine attributes contributes an essential constituent for the extension of the tradespace exploration methodology from the single system to the SoS level, allowing for the comparison of many SoS alternatives on a common cost-utility basis.

A Meta-Model for Product Service Systems of Systems

“…Therefore, significant research exists on 1) SoS architecting, i.e. defining SoS configuration/ reconfiguration and describing the relationships (or collaboration (DiMario et al, 2009)) between SoS constituent systems (Chattopadhyay et al, 2009;Dagli et al, 2013), and 2) SoS management, i.e. describing SoS stakeholders' relationships, characterizing cross-enterprise organization to support systems interoperability, and studying organization's business strategy (Carlock and Fenton, 2001).…”

“…These dimensions seem to relate to organisations, management, and enterprise (multi-organisational) relationships. More specifically, Dagli et al (2013) (Chattopadhyay et al, 2009;Gomes et al, 2015) also considers the notion of capability. For example, Gomes et al (2015) define a capability as "A capability the ability of a system to provide a given functionality by performing a set of tasks" and point out the importance of capabilities in discovering and composing constituent systems within a SoS.…”

“…The SoS attributes and levels of attributes (e.g. levels of performance) usually express the SoS stakeholders or decision-makers' needs (Chattopadhyay et al, 2009) and values (e.g. the value the stakeholders assign to a performance) (Ricci et al, 2014).…”

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

“…The diversity in domains where it has been used is growing and positive results have already being obtained for selecting design concepts of manned and unmanned space systems [Ross et al., ; Golkar and Crawley, ; Shaw et al., ], aircrafts [Lewis, Tackett, and Mattson, ], structural elements [Mattson and Messac, ; Lewis et al., ], military operations [Hong, Wee, and Kiat, ], and Stirling engines [Smirnov and Golkar, ]. In addition, tradespace exploration has been found effective in supporting concept selection of systems of systems both in research [Chattopadhyay, Ross, and Rhodes, , ] and in practice [Salado, ]. As a matter of fact, the method has now been infused as a standard practice for concept development at the Jet Propulsion Laboratory as part of their Concept Maturity Levels [Randii et al., ] and work is set forth for achieving common processes and tools at DoD [Spero et al., ].…”

Revisiting the Tradespace Exploration Paradigm: Structuring the Exploration Process

“… Systems of systems require higher order modeling of SoS-value, including sophisticated combining of component system attributes 31 , as well as consideration of the impact of inheritance of legacy systems 6 .  Dynamic considerations are difficult to visualize and analyze, especially with the large growth of dataset size (a tradespace per epoch and many such orderings of these to generate the eras), but metrics can be used to screen through the dataset to identify "interesting" designs for further consideration.…”