Christine M. Edwards, Roshi Rose Nilchiani, Ian M. Miller
Societies depend on various complex and highly interconnected systems, leading to increasing interest in methods for managing the resilience of these complex systems and the risks associated with their disruption or failure. Identifying and localizing tipping points, or phase transitions, in complex systems is essential for predicting system behavior but a difficult challenge when there are many interacting elements. Systems may transition from stable to unstable at critical tipping‐point thresholds and potentially collapse. One of the suggested approaches in literature is to measure a complex system's resilience to collapse by modeling the system as a network, reducing the network behavior to a simpler model, and then measuring the resulting model's stability. In particular, Gao and colleagues introduced a methodology in 2016 that introduces a resilience index to measure precariousness (the distance to tipping points). However, those mathematical reductions can cause information loss from reducing the topological complexity of the system. Herein, the authors introduce a new methodology that more‐accurately predicts the location of tipping points in networked systems and their precariousness with respect to those tipping points by integrating two approaches: (1) a new measurement of a system's topological complexity using graph energy (created based on molecular orbital theory) and; (2) the resilience index method from Gao et al. This new approach is tested in three separate case studies involving ecosystem collapse, supply chain sustainability, and disruptive technology. Results show a shift in tipping‐point locations correlated with graph energy. The authors present an equation that corrects errors introduced as a result of the model reduction, providing a measurement of precariousness that gives insight into how a complex system's topology affects the location of its tipping points.
{"title":"Impact of graph energy on a measurement of resilience for tipping points in complex systems","authors":"Christine M. Edwards, Roshi Rose Nilchiani, Ian M. Miller","doi":"10.1002/sys.21749","DOIUrl":"https://doi.org/10.1002/sys.21749","url":null,"abstract":"Societies depend on various complex and highly interconnected systems, leading to increasing interest in methods for managing the resilience of these complex systems and the risks associated with their disruption or failure. Identifying and localizing tipping points, or phase transitions, in complex systems is essential for predicting system behavior but a difficult challenge when there are many interacting elements. Systems may transition from stable to unstable at critical tipping‐point thresholds and potentially collapse. One of the suggested approaches in literature is to measure a complex system's resilience to collapse by modeling the system as a network, reducing the network behavior to a simpler model, and then measuring the resulting model's stability. In particular, Gao and colleagues introduced a methodology in 2016 that introduces a resilience index to measure precariousness (the distance to tipping points). However, those mathematical reductions can cause information loss from reducing the topological complexity of the system. Herein, the authors introduce a new methodology that more‐accurately predicts the location of tipping points in networked systems and their precariousness with respect to those tipping points by integrating two approaches: (1) a new measurement of a system's topological complexity using graph energy (created based on molecular orbital theory) and; (2) the resilience index method from Gao et al. This new approach is tested in three separate case studies involving ecosystem collapse, supply chain sustainability, and disruptive technology. Results show a shift in tipping‐point locations correlated with graph energy. The authors present an equation that corrects errors introduced as a result of the model reduction, providing a measurement of precariousness that gives insight into how a complex system's topology affects the location of its tipping points.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"115 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christine M. Edwards, Roshi Rose Nilchiani, Ian M. Miller
Societies depend on various complex and highly interconnected systems, leading to increasing interest in methods for managing the resilience of these complex systems and the risks associated with their disruption or failure. Identifying and localizing tipping points, or phase transitions, in complex systems is essential for predicting system behavior but a difficult challenge when there are many interacting elements. Systems may transition from stable to unstable at critical tipping‐point thresholds and potentially collapse. One of the suggested approaches in literature is to measure a complex system's resilience to collapse by modeling the system as a network, reducing the network behavior to a simpler model, and then measuring the resulting model's stability. In particular, Gao and colleagues introduced a methodology in 2016 that introduces a resilience index to measure precariousness (the distance to tipping points). However, those mathematical reductions can cause information loss from reducing the topological complexity of the system. Herein, the authors introduce a new methodology that more‐accurately predicts the location of tipping points in networked systems and their precariousness with respect to those tipping points by integrating two approaches: (1) a new measurement of a system's topological complexity using graph energy (created based on molecular orbital theory) and; (2) the resilience index method from Gao et al. This new approach is tested in three separate case studies involving ecosystem collapse, supply chain sustainability, and disruptive technology. Results show a shift in tipping‐point locations correlated with graph energy. The authors present an equation that corrects errors introduced as a result of the model reduction, providing a measurement of precariousness that gives insight into how a complex system's topology affects the location of its tipping points.
{"title":"Impact of graph energy on a measurement of resilience for tipping points in complex systems","authors":"Christine M. Edwards, Roshi Rose Nilchiani, Ian M. Miller","doi":"10.1002/sys.21749","DOIUrl":"https://doi.org/10.1002/sys.21749","url":null,"abstract":"Societies depend on various complex and highly interconnected systems, leading to increasing interest in methods for managing the resilience of these complex systems and the risks associated with their disruption or failure. Identifying and localizing tipping points, or phase transitions, in complex systems is essential for predicting system behavior but a difficult challenge when there are many interacting elements. Systems may transition from stable to unstable at critical tipping‐point thresholds and potentially collapse. One of the suggested approaches in literature is to measure a complex system's resilience to collapse by modeling the system as a network, reducing the network behavior to a simpler model, and then measuring the resulting model's stability. In particular, Gao and colleagues introduced a methodology in 2016 that introduces a resilience index to measure precariousness (the distance to tipping points). However, those mathematical reductions can cause information loss from reducing the topological complexity of the system. Herein, the authors introduce a new methodology that more‐accurately predicts the location of tipping points in networked systems and their precariousness with respect to those tipping points by integrating two approaches: (1) a new measurement of a system's topological complexity using graph energy (created based on molecular orbital theory) and; (2) the resilience index method from Gao et al. This new approach is tested in three separate case studies involving ecosystem collapse, supply chain sustainability, and disruptive technology. Results show a shift in tipping‐point locations correlated with graph energy. The authors present an equation that corrects errors introduced as a result of the model reduction, providing a measurement of precariousness that gives insight into how a complex system's topology affects the location of its tipping points.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"16 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Imke Hoppe, Willem Hagemann, Ingo Stierand, Axel Hahn, Andre Bolles
Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the “explainability” of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that “learning from life” requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual “how”, dives into the intricacies and showcases (missing) interconnectedness in the state of research.
{"title":"Challenges for trustworthy autonomous vehicles: Let us learn from life","authors":"Imke Hoppe, Willem Hagemann, Ingo Stierand, Axel Hahn, Andre Bolles","doi":"10.1002/sys.21744","DOIUrl":"https://doi.org/10.1002/sys.21744","url":null,"abstract":"Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the “explainability” of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that “learning from life” requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual “how”, dives into the intricacies and showcases (missing) interconnectedness in the state of research.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Imke Hoppe, Willem Hagemann, Ingo Stierand, Axel Hahn, Andre Bolles
Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the “explainability” of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that “learning from life” requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual “how”, dives into the intricacies and showcases (missing) interconnectedness in the state of research.
{"title":"Challenges for trustworthy autonomous vehicles: Let us learn from life","authors":"Imke Hoppe, Willem Hagemann, Ingo Stierand, Axel Hahn, Andre Bolles","doi":"10.1002/sys.21744","DOIUrl":"https://doi.org/10.1002/sys.21744","url":null,"abstract":"Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the “explainability” of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that “learning from life” requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual “how”, dives into the intricacies and showcases (missing) interconnectedness in the state of research.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"96 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.
我们利用分层深度强化学习和基于代理的模拟迭代，为系统的系统（SoS）引入了一种新的资源管理方法。这种方法的一个关键创新点是，它能够在自上而下的系统管理与单个系统的自主性之间取得平衡。这是通过为每个系统动态分配资源来实现的，从而修改了它们可以自主选择的选项范围。这种动态选项调整是管理集中式效率和分散式系统自主行动之间权衡的有力方法，使 SoS 既能保持系统的自主性，又能确保高效的 SoS 治理。该方法通过案例研究得到了验证，不仅展示了学习框架的潜力和功效，还揭示了使用这种方法，微小的性能牺牲如何能够带来资源效率的大幅提高。
{"title":"The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning","authors":"Qiliang Chen, Babak Heydari","doi":"10.1002/sys.21747","DOIUrl":"https://doi.org/10.1002/sys.21747","url":null,"abstract":"We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"23 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139856177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.
我们利用分层深度强化学习和基于代理的模拟迭代，为系统的系统（SoS）引入了一种新的资源管理方法。这种方法的一个关键创新点是，它能够在自上而下的系统管理与单个系统的自主性之间取得平衡。这是通过为每个系统动态分配资源来实现的，从而修改了它们可以自主选择的选项范围。这种动态选项调整是管理集中式效率和分散式系统自主行动之间权衡的有力方法，使 SoS 既能保持系统的自主性，又能确保高效的 SoS 治理。该方法通过案例研究得到了验证，不仅展示了学习框架的潜力和功效，还揭示了使用这种方法，微小的性能牺牲如何能够带来资源效率的大幅提高。
{"title":"The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning","authors":"Qiliang Chen, Babak Heydari","doi":"10.1002/sys.21747","DOIUrl":"https://doi.org/10.1002/sys.21747","url":null,"abstract":"We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"5 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers.
{"title":"Mapping industry workforce needs to academic curricula – A workforce development effort in model‐based systems engineering","authors":"Aditya Akundi, Wilma Ankobiah","doi":"10.1002/sys.21745","DOIUrl":"https://doi.org/10.1002/sys.21745","url":null,"abstract":"Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"11 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers.
{"title":"Mapping industry workforce needs to academic curricula – A workforce development effort in model‐based systems engineering","authors":"Aditya Akundi, Wilma Ankobiah","doi":"10.1002/sys.21745","DOIUrl":"https://doi.org/10.1002/sys.21745","url":null,"abstract":"Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"207 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139826147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel P. Vieira, Í. A. Fonseca, Kazuo Nishimoto, Gustavo R. S. Assi, Henrique M. Gaspar, Donna H. Rhodes
With the demand to reduce the release of CO2‐rich gases into the atmosphere, the offshore industry is turning to systems for gas capture and storage. The construction of a cave for that purpose is planned in the salt layer of the Santos Basin, Brazil; however, numerous factors bring uncertainties to the project. We propose combining systems engineering methods and digital twin technology to enable system concept and execution aiming at value robustness. The system is modeled in mission, subsystems, components, and design attributes. System utilities and costs are estimated for a range of viable solutions based on the relevance of each attribute to the stakeholders. The alternatives are evaluated using the Epoch‐Era Analysis framework for analyzing the system's performance over time in changing future scenarios. The system model also outlines a digital twin concept and identifies how it might support salt cave design and operations. Finally, the potential of tuning and improving system evaluation based on gathered data is examined, and measures toward further digital twin development are recommended.
{"title":"Leveraging epoch‐era analysis and digital twin for effective system concept and execution: A CO2 storage salt‐cave project","authors":"Daniel P. Vieira, Í. A. Fonseca, Kazuo Nishimoto, Gustavo R. S. Assi, Henrique M. Gaspar, Donna H. Rhodes","doi":"10.1002/sys.21741","DOIUrl":"https://doi.org/10.1002/sys.21741","url":null,"abstract":"With the demand to reduce the release of CO2‐rich gases into the atmosphere, the offshore industry is turning to systems for gas capture and storage. The construction of a cave for that purpose is planned in the salt layer of the Santos Basin, Brazil; however, numerous factors bring uncertainties to the project. We propose combining systems engineering methods and digital twin technology to enable system concept and execution aiming at value robustness. The system is modeled in mission, subsystems, components, and design attributes. System utilities and costs are estimated for a range of viable solutions based on the relevance of each attribute to the stakeholders. The alternatives are evaluated using the Epoch‐Era Analysis framework for analyzing the system's performance over time in changing future scenarios. The system model also outlines a digital twin concept and identifies how it might support salt cave design and operations. Finally, the potential of tuning and improving system evaluation based on gathered data is examined, and measures toward further digital twin development are recommended.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139140922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhihui Xu, Gaoguang Yang, Yi Lu, Jiaxin Xue, Guanyin Wu, Bingxuan Ren, Shan Fu
The evolution of the mechanism of human behavior formation analysis has significantly influenced the development of human reliability analysis (HRA), which aims to calculate human error probability (HEP) with performance shaping factors (PSFs). This paper reviews the typical HRA methods in different generations, the role of PSFs, and their interrelation‐ships in human risk modeling, with the background of nuclear power plants (NPPs). In a retrospective of typical HRA methods, PSF plays a fundamental role in assessing human performance during task operation. However, the subjectivity in defining and evaluating PSFs often leads to a partial representation of human behavior characteristics and human risk evolution, resulting in the neglect of PSF inter‐relationships and conservative HEP estimation. Recent studies have emphasized employing simulation platforms to simulate the task process and obtain data relevant to PSFs that can enable the exploration of the mutual effects to support the calculation of HEP more accurately. Compared to certain previous methods involving over‐simplification and inappropriate assumptions resulting in inaccurate results, current HRA methods are prone to the construction of HEP models based on objective data acquisition and dynamic calculations with process models. This shift enables a better illustration of the intricate relationships among PSFs. Reflecting on the current trend of HRA methodology, this paper proposes a possible PSF quantification based on physiological measurement providing accessible and objective data. It improves the shortcomings in data scarcity and time‐invariance of HEP calculation, thus more accurately and realistically responds to the accumulation and fluctuation of human risks throughout a task.
人类行为形成分析机制的演变极大地影响了人类可靠性分析（HRA）的发展，而人类可靠性分析旨在利用性能影响因素（PSFs）计算人类出错概率（HEP）。本文以核电站（NPP）为背景，回顾了不同时代的典型 HRA 方法、PSFs 的作用以及它们在人为风险建模中的相互关系。在对典型的人的风险评估方法的回顾中，PSF 在评估任务操作过程中人的表现方面起着根本性的作用。然而，在定义和评估 PSF 时的主观性往往会导致对人类行为特征和人类风险演变的片面表述，从而导致忽略 PSF 的相互关系和保守的 HEP 估算。近期的研究强调利用模拟平台来模拟任务过程，获取 PSF 的相关数据，从而探索相互影响，为更准确地计算 HEP 提供支持。与以往某些过度简化和不恰当假设导致结果不准确的方法相比，目前的 HRA 方法容易在客观数据采集和流程模型动态计算的基础上构建 HEP 模型。这种转变能够更好地说明 PSF 之间错综复杂的关系。针对当前 HRA 方法的发展趋势，本文提出了一种基于生理测量的 PSF 量化方法，该方法可提供可获取的客观数据。该方法改善了 HEP 计算中数据稀缺和时变性的缺点，从而更准确、更真实地反应了整个任务过程中人类风险的积累和波动。
{"title":"Path to modeling dynamic performance shaping factors in nuclear power plants operation – A review","authors":"Zhihui Xu, Gaoguang Yang, Yi Lu, Jiaxin Xue, Guanyin Wu, Bingxuan Ren, Shan Fu","doi":"10.1002/sys.21742","DOIUrl":"https://doi.org/10.1002/sys.21742","url":null,"abstract":"The evolution of the mechanism of human behavior formation analysis has significantly influenced the development of human reliability analysis (HRA), which aims to calculate human error probability (HEP) with performance shaping factors (PSFs). This paper reviews the typical HRA methods in different generations, the role of PSFs, and their interrelation‐ships in human risk modeling, with the background of nuclear power plants (NPPs). In a retrospective of typical HRA methods, PSF plays a fundamental role in assessing human performance during task operation. However, the subjectivity in defining and evaluating PSFs often leads to a partial representation of human behavior characteristics and human risk evolution, resulting in the neglect of PSF inter‐relationships and conservative HEP estimation. Recent studies have emphasized employing simulation platforms to simulate the task process and obtain data relevant to PSFs that can enable the exploration of the mutual effects to support the calculation of HEP more accurately. Compared to certain previous methods involving over‐simplification and inappropriate assumptions resulting in inaccurate results, current HRA methods are prone to the construction of HEP models based on objective data acquisition and dynamic calculations with process models. This shift enables a better illustration of the intricate relationships among PSFs. Reflecting on the current trend of HRA methodology, this paper proposes a possible PSF quantification based on physiological measurement providing accessible and objective data. It improves the shortcomings in data scarcity and time‐invariance of HEP calculation, thus more accurately and realistically responds to the accumulation and fluctuation of human risks throughout a task.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"7 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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