Autonomous vehicles need high reliability for consumer acceptance. A high-reliability system is c... more Autonomous vehicles need high reliability for consumer acceptance. A high-reliability system is capable of relatively error-free operations over extended durations making consistently good decisions that result in highly reliable and safe operations. High reliability is an imperative for autonomous networked vehicles. This paper reviews currently available approaches for developing high-reliability systems. We pose the question whether reliability requirements for self-driving vehicles should be similar to those for other high-reliability systems or should we draw on and extrapolate the body of knowledge for human-operated systems. We provide an analysis that helps with answering this question.
As systems continue to grow in scope, scale, and complexity, the ability to model, analyze, and d... more As systems continue to grow in scope, scale, and complexity, the ability to model, analyze, and design them has become a critical systems engineering challenge. Over the past decade and a half, several model-based approaches (e.g., SysML, OPM) have been developed and employed for modeling and analyzing complex systems. These methods require familiarity with specialized engineering notation on the part of stakeholders. Unfamiliar with these modeling notations, nontechnical stakeholders are unable to contribute to upfront engineering increasing the risk of extraneous design iterations and rework that invariably lead to schedule delays and cost over-runs. Today, there is an even bigger challenge given that systems need to adapt to changing operational environments and new regulations, while having the requisite flexibility to seamlessly and opportunistically integrate emerging, new, and high-payoff technologies. In this chapter, I showed that by transforming system models into system stories all stakeholders can be meaningfully engaged. Specifically, the different stakeholders can interactively execute their own stories in virtual worlds and thereby increase their understanding and contribution to upfront engineering. I called this model-based interactive storytelling (MBIS).
System complexity continues to escalate with the rapid advances of technology and research in res... more System complexity continues to escalate with the rapid advances of technology and research in resilience engineering is of great interest as the result of managing such complexity. In order to fully understand resilience engineering and it's supporting elements, a resilience framework is proposed outlining supporting elemental definitions and their complementary roles. The challenge in conducting decision-making can be assisted by quantifying the elements of the resilience framework to make informed decisions. This paper outlines a resilience framework and focuses on adaptability as a key resilience characteristic.
The way system architects typically decompose a high-level system architecture has an outsized im... more The way system architects typically decompose a high-level system architecture has an outsized impact on interface complexity of systems and system-of-systems (SoS). In existing practice, systems are decomposed based on empirical knowledge of system architects and the prevailing architecture of organizations responsible for developing systems. The decomposition of systems (and SoS) involves aspects of engineering, management, and social sciences. How the decomposition approach breaks down a complex system determines the effectiveness of the integration strategy. In this article, we recommend that a complex system (and more specifically, a complex sociotechnical system) should be decomposed and aligned along five perspectives or layers: operation, function, physical, implementation, and organization. At each layer, the system architecture exhibits a certain level of complexity. Aligning modules at each layer with modules at the level above can help reduce the complexity of the overall system making it more manageable. This article presents a system architecting methodology that employs interlevel and intralevel dependency matrix (I2DM) to enable this approach. The I2DM methodology employs stochastic optimization in combination with heuristics to align modules in the different layers and then identifies problematic interactions that could pose problems in system integration. The article also describes how techniques from pattern recognition, data analytics, and clustering can be applied to system architecture models to formulate and assess the efficacy of a system integration strategy. The ontology underlying the systems architecting and integration strategy is also presented.
Forming or participating in a virtual enterprise is considered an effective strategy to exploit m... more Forming or participating in a virtual enterprise is considered an effective strategy to exploit market opportunities that cannot be pursued individually by the partnering organizations. In practice, however, virtual enterprise formation is quite complicated. There are no systematic methodologies or tools to assist decision-makers in: (a) planning and analyzing the formation of a virtual enterprise; and (b) selecting compatible partners/suppliers
Autonomous vehicles need high reliability for consumer acceptance. A high-reliability system is c... more Autonomous vehicles need high reliability for consumer acceptance. A high-reliability system is capable of relatively error-free operations over extended durations making consistently good decisions that result in highly reliable and safe operations. High reliability is an imperative for autonomous networked vehicles. This paper reviews currently available approaches for developing high-reliability systems. We pose the question whether reliability requirements for self-driving vehicles should be similar to those for other high-reliability systems or should we draw on and extrapolate the body of knowledge for human-operated systems. We provide an analysis that helps with answering this question.
As systems continue to grow in scope, scale, and complexity, the ability to model, analyze, and d... more As systems continue to grow in scope, scale, and complexity, the ability to model, analyze, and design them has become a critical systems engineering challenge. Over the past decade and a half, several model-based approaches (e.g., SysML, OPM) have been developed and employed for modeling and analyzing complex systems. These methods require familiarity with specialized engineering notation on the part of stakeholders. Unfamiliar with these modeling notations, nontechnical stakeholders are unable to contribute to upfront engineering increasing the risk of extraneous design iterations and rework that invariably lead to schedule delays and cost over-runs. Today, there is an even bigger challenge given that systems need to adapt to changing operational environments and new regulations, while having the requisite flexibility to seamlessly and opportunistically integrate emerging, new, and high-payoff technologies. In this chapter, I showed that by transforming system models into system stories all stakeholders can be meaningfully engaged. Specifically, the different stakeholders can interactively execute their own stories in virtual worlds and thereby increase their understanding and contribution to upfront engineering. I called this model-based interactive storytelling (MBIS).
System complexity continues to escalate with the rapid advances of technology and research in res... more System complexity continues to escalate with the rapid advances of technology and research in resilience engineering is of great interest as the result of managing such complexity. In order to fully understand resilience engineering and it's supporting elements, a resilience framework is proposed outlining supporting elemental definitions and their complementary roles. The challenge in conducting decision-making can be assisted by quantifying the elements of the resilience framework to make informed decisions. This paper outlines a resilience framework and focuses on adaptability as a key resilience characteristic.
The way system architects typically decompose a high-level system architecture has an outsized im... more The way system architects typically decompose a high-level system architecture has an outsized impact on interface complexity of systems and system-of-systems (SoS). In existing practice, systems are decomposed based on empirical knowledge of system architects and the prevailing architecture of organizations responsible for developing systems. The decomposition of systems (and SoS) involves aspects of engineering, management, and social sciences. How the decomposition approach breaks down a complex system determines the effectiveness of the integration strategy. In this article, we recommend that a complex system (and more specifically, a complex sociotechnical system) should be decomposed and aligned along five perspectives or layers: operation, function, physical, implementation, and organization. At each layer, the system architecture exhibits a certain level of complexity. Aligning modules at each layer with modules at the level above can help reduce the complexity of the overall system making it more manageable. This article presents a system architecting methodology that employs interlevel and intralevel dependency matrix (I2DM) to enable this approach. The I2DM methodology employs stochastic optimization in combination with heuristics to align modules in the different layers and then identifies problematic interactions that could pose problems in system integration. The article also describes how techniques from pattern recognition, data analytics, and clustering can be applied to system architecture models to formulate and assess the efficacy of a system integration strategy. The ontology underlying the systems architecting and integration strategy is also presented.
Forming or participating in a virtual enterprise is considered an effective strategy to exploit m... more Forming or participating in a virtual enterprise is considered an effective strategy to exploit market opportunities that cannot be pursued individually by the partnering organizations. In practice, however, virtual enterprise formation is quite complicated. There are no systematic methodologies or tools to assist decision-makers in: (a) planning and analyzing the formation of a virtual enterprise; and (b) selecting compatible partners/suppliers
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