Abheek Chatterjee, Cade Helbig, Richard Malak, Astrid Layton
{"title":"系统设计中弹性系统的图论方法比较","authors":"Abheek Chatterjee, Cade Helbig, Richard Malak, Astrid Layton","doi":"10.1115/1.4062231","DOIUrl":null,"url":null,"abstract":"Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life such as power grids, supply chains, and disaster monitoring and tracking services. Their resilience (being able to withstand and recover from disruptions) is a critical attribute whose evaluation is nontrivial and requires detailed disruption models. Engineers rely on heuristics (such as redundancy and localized capacity) for achieving resilience. However, excessive reliance on these qualitative guidelines can result in unacceptable operation costs, erosion of profits, over-consumption of natural resources, or unacceptable levels of waste or emissions. Graph-theoretic approaches provide a potential solution to this challenge as they can evaluate architectural characteristics without needing detailed performance simulations, supporting their use in early stage SoS architecture selection. However, no consensus exists as to which graph-theoretic metrics are most valuable for SoS design and how they should be included in the design process. In this work, multiple graph-theoretic approaches are analyzed and compared, on a common platform, for their use as design tools for resilient SoS. The metrics central point dominance, modularity, specialized predator ratio, generalization, vulnerability, and degree of system order are found to be viable options for the development of early stage decision-support tools for resilient SoS design.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Comparison of Graph-Theoretic Approaches for Resilient System of Systems Design\",\"authors\":\"Abheek Chatterjee, Cade Helbig, Richard Malak, Astrid Layton\",\"doi\":\"10.1115/1.4062231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. 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A Comparison of Graph-Theoretic Approaches for Resilient System of Systems Design
Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life such as power grids, supply chains, and disaster monitoring and tracking services. Their resilience (being able to withstand and recover from disruptions) is a critical attribute whose evaluation is nontrivial and requires detailed disruption models. Engineers rely on heuristics (such as redundancy and localized capacity) for achieving resilience. However, excessive reliance on these qualitative guidelines can result in unacceptable operation costs, erosion of profits, over-consumption of natural resources, or unacceptable levels of waste or emissions. Graph-theoretic approaches provide a potential solution to this challenge as they can evaluate architectural characteristics without needing detailed performance simulations, supporting their use in early stage SoS architecture selection. However, no consensus exists as to which graph-theoretic metrics are most valuable for SoS design and how they should be included in the design process. In this work, multiple graph-theoretic approaches are analyzed and compared, on a common platform, for their use as design tools for resilient SoS. The metrics central point dominance, modularity, specialized predator ratio, generalization, vulnerability, and degree of system order are found to be viable options for the development of early stage decision-support tools for resilient SoS design.
期刊介绍:
The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping