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The International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent. [ABSTRACT FROM AUTHOR]

This study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability. [ABSTRACT FROM AUTHOR]

In an increasingly interconnected & cyber-physical world, complexity is often cited as the root cause of adverse project outcomes, including cost-overruns and schedule delays. This realization has prompted calls for better complexity management, which hinges on the ability to recognize and measure complexity early in the design process. However, while numerous complexity measures (CMs) have been promulgated, there is limited agreement about "how" complexity should be measured and what a good measure should entail. In this paper, we propose a framework for benchmarking CMs in terms of how well they are able to detect systematic variation along key aspects of complexity growth. Specifically, the literature is consistent in expecting that complexity growth is correlated with increases in size, number of interconnections, and randomness of the system architecture. Therefore, to neutrally compare six representative CMs, we synthetically create a set of system architectures that systematically vary across each dimension. We find that none of the measures are able to detect changes in all three dimensions simultaneously, though several are consistent in their response to one or two. We also find that there is a dichotomy in the literature regarding the archetype of systems that are considered as complex: CMs developed by researchers focused on physics-based (e.g., aircraft) tend to emphasize interconnectedness and structure whereas flow-based (e.g., the power grid) focus on size. Our findings emphasize the need for more careful validation across proposed measures. Our framework provides a path to enable shared progress towards the goal of better complexity management. [ABSTRACT FROM AUTHOR]

Decomposition is a dominant design strategy because it enables complex problems to be broken up into loosely coupled modules that are easier to manage and can be designed in parallel. However, contrary to widely held expectations, we show that complexity can increase substantially when natural system modules are fully decoupled from one another to support parallel design. Drawing on detailed empirical evidence from a NASA space robotics field experiment, we explain how new information is introduced into the design space through three complexity addition mechanisms of the decomposition process: interface creation, functional allocation, and second-order effects. These findings have important implications for how modules are selected early in the design process and how future decomposition approaches should be developed. Although it is well known that complex systems are rarely fully decomposable and that the decoupling process necessitates additional design work, the literature is predominantly focused on reordering, clustering, and/or grouping-based approaches to define module boundaries within a fixed system representation. Consequently, these approaches are unable to account for the (often significant) new information that is added to the design space through the decomposition process. We contend that the observed mechanisms of complexity growth need to be better accounted for during the module selection process in order to avoid unexpected downstream costs. With this work, we lay a foundation for valuing these complexity-induced impacts to performance, schedule, and cost, earlier in the decomposition process. [ABSTRACT FROM AUTHOR]