Abstract Traditional document‐based practices in systems engineering are being transitioned to model‐based ones. Adoption of model‐based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)’s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.

Abstract The integration of complex systems is an important aspect of systems engineering. This paper defines six integration principles to consider when planning and executing system development and integration processes. This paper describes these integration principles and validates them through a historical system survey. The 14 systems studied represent both integration successes and integration failures to show the universal applicability of the principles. The historical system analysis supported the validity of four of the six principles. The validity of one of the remaining principles could not be concluded through the evidence gathered. The validity of the final principle was inconclusive due to a lack of evidence found. Understanding these principles and how to apply them to an integration program will increase the probability of successful integration of future systems.

Abstract There is an urgent need for more efficient and resilient infrastructure systems to support a growing population with increasingly scarce resources worldwide. As the demand for limited natural and man‐made resources grows, improved methods for resolving anticipated and unforeseen conflicts of availability are needed. System automation has broadly been adopted for efficiency optimization and resource deconfliction via preplanned actions and responses to anticipated needs. However, human intervention is still relied upon up to resolve emergent issues for which automation lacks the flexibility and adaptability to resolve. In this research, a context‐based decision model for system automation is presented that uses satisficing heuristics to deconflict shared resources without preplanning or human intervention. Via modeling and simulation, the presented model and existing rule‐based algorithms were applied to an air traffic management problem to compare performance with respect to: (a) system efficiency, (b) standard deviation of efficiency, and (c) system stability as a measure of disruption avoidance. Simulation results demonstrate that the presented model concurrently supports efficiency optimization and disruption avoidance for airspace deconfliction, averaging 9.7% higher system efficiency and a 1.5% lower standard deviation of efficiency than the existing rule‐based standard for aircraft collision avoidance (based on an air traffic density of 45 aircraft per 10 000 square nautical miles). The model presented here is extensible to engineered systems that rely on shared, finite resources for mission execution. This research is relevant to the domains of critical infrastructure management, risk management, distributed control systems, and mission assurance.

Systems Engineering is a discipline whose responsibility it is to create and operate technologically enabled systems that satisfy stakeholder needs throughout their life cycle. Systems engineers reduce ambiguity by clearly defining stakeholder needs and customer requirements, they focus creativity by developing a system’s architecture and design and they manage the system’s complexity over time.

Abstract Dynamic and real‐time adaptive configuration of Cyber‐Physical Systems (CPSs) results in increased complexity due to a variety of heterogeneous and interdependent variables and creates unique challenges. For example, (a) Emergent Behavior: How do we ensure that system constituents dynamically and adaptively collaborate to produce a consistent repeatable functionality while supporting the capability to upgrade the individual entities through technology infusion; (b) Scale: How do we ensure scalability of these systems by managing complexity; and (c) Risk Management: How do we evaluate and manage the risks associated with the connection and interdependencies of heterogenous systems. Design and development of this new generation of CPSs can be viewed through the lens of System‐of‐Systems (SoS) methodology which is designed to analyze and assess the evolving topologies created by interactions within a large complex system operating in dynamic and uncertain environment. In this paper, we propose the use of several SoS tools and techniques for the analysis and design of next‐generation CPSs. Our SoS methodologies address features such as diversity of component systems, complex hierarchical structures, dynamic and emergent behavior, and interactions between components. Therefore, they are suitable to treat some of the challenging features of CPSs. However, it is necessary to modify these methodologies to address specific aspects of CPSs. Constraints and metrics from SoS methodology, applied to the design space, will support decision on component systems and the topology of their connections, and provide a set of “good designs,” with desired characteristics.

Systems Engineering is a discipline whose responsibility it is to create and operate technologically enabled systems that satisfy stakeholder needs throughout their life cycle. Systems engineers reduce ambiguity by clearly defining stakeholder needs and customer requirements, they focus creativity by developing a system’s architecture and design and they manage the system’s complexity over time.

Abstract Addressing modern societal challenges often requires the consideration of sociotechnical systems. Sociotechnical systems consist of technological, organizational, and social systems interacting to achieve a goal. Developing effective approaches to modeling sociotechnical systems could enable policy makers to better understand the implications of policy options prior to implementation. Through past work, the authors evaluated a modeling methodology intended to accomplish that goal. While the methodology was found to be useful, there were also significant limitations. In this paper, the authors analyze these limitations through the lens of consolidative versus exploratory modeling. The result is a substantial revision of the modeling methodology that moves from a sequential 10‐step process to a multiphase process intended to facilitate exploratory modeling and detect unintended policy consequences.

Abstract System of interest (SoI) failures can sometimes be traced to an unexpected behavior occurring within another system that is a member of the system of systems (SoS) with the SoI. This article presents a method for use when designing an SoI that helps to analyze an SoS for unexpected behaviors from existing SoS members during the SoI's conceptual functional modeling phase of system architecture. The concept of irrationality initiators—unanticipated or unexpected failure flows emitted from one system that adversely impact an SoI, which appear to be impossible or irrational to engineers developing the new system—is introduced and implemented in a quantitative risk analysis method. The method is implemented in the failure flow identification and propagation framework to yield a probability distribution of failure paths through an SoI in the SoS. An example of a network of autonomous vehicles operating in a partially denied environment is presented to demonstrate the method. The method presented in this paper allows practitioners to more easily identify potential failure paths and prioritize fixing vulnerabilities in an SoI during functional modeling when significant changes can still be made with minimal impact to cost and schedule.

International audience