Chris Swickline, Thomas A Mazzuchi, Shahram Sarkani
Abstract An essential role of systems engineering is to aid in overcoming complexity during development. As system complexity increases, a greater number of organizations are being asked to contribute architecture/design content for systems development. An essential challenge to overcome is how to ensure digital continuity in connecting system models to form systems of systems (SoS) models. Leveraging recent developments in digital engineering (DE) and model‐based systems engineering (MBSE) this paper presents a SysML model federation methodology enabling multiple collaborating organizations to contribute system models and form a descriptive SoS architecture model. Systems engineers applying this methodology are able to reuse constituent system model content to describe the larger SoS behavior and structure while promoting reusability and commonality and are therefore better able to ensure correctness and consistency across development. First, a background through the review of SoS engineering (SoSE) and DE/MBSE literature is provided. Then an MBSE process along with specific methods tailoring the use of SysML within the Cameo Enterprise Architecture tool is presented. This work builds upon a previously published Systems Architecture Model (SAM) development method and is designed to permit style diversity across peer constituent models and ensure that every piece of data has an authoritative source of truth (ASOT) within the federation. This paper then presents the application of this methodology to the Ranger Lunar Rover system by making use of its openly published SysML model. Finally, the benefits and challenges of this methodology are discussed prior to drawing closing conclusions and identifying future areas of research.
{"title":"A methodology for developing SoS architectures using SysML model federation","authors":"Chris Swickline, Thomas A Mazzuchi, Shahram Sarkani","doi":"10.1002/sys.21727","DOIUrl":"https://doi.org/10.1002/sys.21727","url":null,"abstract":"Abstract An essential role of systems engineering is to aid in overcoming complexity during development. As system complexity increases, a greater number of organizations are being asked to contribute architecture/design content for systems development. An essential challenge to overcome is how to ensure digital continuity in connecting system models to form systems of systems (SoS) models. Leveraging recent developments in digital engineering (DE) and model‐based systems engineering (MBSE) this paper presents a SysML model federation methodology enabling multiple collaborating organizations to contribute system models and form a descriptive SoS architecture model. Systems engineers applying this methodology are able to reuse constituent system model content to describe the larger SoS behavior and structure while promoting reusability and commonality and are therefore better able to ensure correctness and consistency across development. First, a background through the review of SoS engineering (SoSE) and DE/MBSE literature is provided. Then an MBSE process along with specific methods tailoring the use of SysML within the Cameo Enterprise Architecture tool is presented. This work builds upon a previously published Systems Architecture Model (SAM) development method and is designed to permit style diversity across peer constituent models and ensure that every piece of data has an authoritative source of truth (ASOT) within the federation. This paper then presents the application of this methodology to the Ranger Lunar Rover system by making use of its openly published SysML model. Finally, the benefits and challenges of this methodology are discussed prior to drawing closing conclusions and identifying future areas of research.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136212985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract One of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5 . Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify , Respond , and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.
{"title":"Knowledge elicitation methodology for evaluation of Internet of Things privacy characteristics in smart cities","authors":"Nil Kilicay‐Ergin, Adrian Barb, Namrata Chaudhary","doi":"10.1002/sys.21726","DOIUrl":"https://doi.org/10.1002/sys.21726","url":null,"abstract":"Abstract One of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5 . Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify , Respond , and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135146206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract As humans play a significant role in maintaining, supporting, and operating systems, it is important to include humans in digital engineering models to ensure that the sociotechnical system is capable of meeting stakeholder requirements. The current research proposes a taxonomy of metrics and taxonomic nomenclature useful for representing humans in systems engineering and integration. This taxonomy is intended to provide a structure for considering metrics in frameworks such as the Objectives and Metrics portion of the human view architecture, as well as in trade studies which may be represented in MBSE models. The proposed taxonomy categorizes human‐centered metrics into four categories, including those affecting system performance, system readiness, interface fitness, and occupational health and safety. It is further recognized that many of these metrics can be affected by the mental or physical state of the human, which are commonly captured in human factors metrics, such as workload or situation awareness. It is proposed that this taxonomy can guide the selection of human‐centered metrics which affect the system's ability to meet system stakeholder requirements.
{"title":"A taxonomy of metrics for human representations in digital engineering","authors":"Michael E. Miller, Emily Spatz","doi":"10.1002/sys.21722","DOIUrl":"https://doi.org/10.1002/sys.21722","url":null,"abstract":"Abstract As humans play a significant role in maintaining, supporting, and operating systems, it is important to include humans in digital engineering models to ensure that the sociotechnical system is capable of meeting stakeholder requirements. The current research proposes a taxonomy of metrics and taxonomic nomenclature useful for representing humans in systems engineering and integration. This taxonomy is intended to provide a structure for considering metrics in frameworks such as the Objectives and Metrics portion of the human view architecture, as well as in trade studies which may be represented in MBSE models. The proposed taxonomy categorizes human‐centered metrics into four categories, including those affecting system performance, system readiness, interface fitness, and occupational health and safety. It is further recognized that many of these metrics can be affected by the mental or physical state of the human, which are commonly captured in human factors metrics, such as workload or situation awareness. It is proposed that this taxonomy can guide the selection of human‐centered metrics which affect the system's ability to meet system stakeholder requirements.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135965997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emily J. Morey, Kevin Galvin, Thomas Riley, R. Eddie Wilson
Abstract Increased demand on rail, due to climate initiatives and passenger numbers, places significant pressure on existing railway operations; specifically on capacity, operational flexibility, and network robustness. These pressures are exacerbated by constraints, which prevent the construction of new track and infrastructure. This results in the need to use existing infrastructure and operating processes. One proposed solution is digitalization, which results in autonomous rail, where automated and connected intelligent transport systems facilitate smart traffic management. However, this generates the challenge of integrating autonomous trains and their associated technologies with existing infrastructure and operations. To understand this issue from an enterprise level, this paper has applied Brian Wilson's Soft Systems Methodology (a variation of Checkland's methodology) to the problem situation. This methodology explores and investigates the existing rail system in Great Britain (UK less Northern Ireland) and its stakeholders. The paper aims to propose a solution into how to transform the legacy system into one which incorporates autonomous operations and ultimately becomes fully autonomous. It culminates in a series of models that are relevant to those with a legitimate interest in the system. The models identify the activities required to analyze whether autonomy is worthwhile and if it is, how to successfully integrate it with legacy operations. Additionally, the models provide the basis for which a formal stakeholder analysis can take place.
{"title":"Application of soft systems methodology to frame the challenges of integrating autonomous trains within a legacy rail operating environment","authors":"Emily J. Morey, Kevin Galvin, Thomas Riley, R. Eddie Wilson","doi":"10.1002/sys.21723","DOIUrl":"https://doi.org/10.1002/sys.21723","url":null,"abstract":"Abstract Increased demand on rail, due to climate initiatives and passenger numbers, places significant pressure on existing railway operations; specifically on capacity, operational flexibility, and network robustness. These pressures are exacerbated by constraints, which prevent the construction of new track and infrastructure. This results in the need to use existing infrastructure and operating processes. One proposed solution is digitalization, which results in autonomous rail, where automated and connected intelligent transport systems facilitate smart traffic management. However, this generates the challenge of integrating autonomous trains and their associated technologies with existing infrastructure and operations. To understand this issue from an enterprise level, this paper has applied Brian Wilson's Soft Systems Methodology (a variation of Checkland's methodology) to the problem situation. This methodology explores and investigates the existing rail system in Great Britain (UK less Northern Ireland) and its stakeholders. The paper aims to propose a solution into how to transform the legacy system into one which incorporates autonomous operations and ultimately becomes fully autonomous. It culminates in a series of models that are relevant to those with a legitimate interest in the system. The models identify the activities required to analyze whether autonomy is worthwhile and if it is, how to successfully integrate it with legacy operations. Additionally, the models provide the basis for which a formal stakeholder analysis can take place.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135959908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Systems engineering can be applied in a broad spectrum of sectors, but only its analysis tool has been applied in the field of water resources management. Because systems engineering has a separate community of practice from water resources, there is little crosstalk between the two fields. As a result, the systems engineering functions that support planning, design, production, procurement, and customer support are not being applied to water systems. Meanwhile, water systems exhibit complexities that have generated a separate field named Integrated Water Resources Management that continues to confuse its followers after several decades. Its methods are applied to a broad spectrum of water issues that affect multiple stakeholders with conflicting interests and involve distinct subsystems, such as water supply or hydropower, as well as combinations of them. Use of systems analysis for such water issues began six decades ago, but it is still a work in progress. Evolving methods of systems engineering offer new possibilities to address problems of water resources management, but they must extend beyond systems analysis, which belongs to multiple disciplines. Examples show possibilities to apply systems engineering methods when water issues exhibit attributes of engineered systems and do not involve social and environmental complexities that cannot be included in system boundaries. Collaboration among systems engineering and water resources management would offer a fertile test bed to advance both fields.
{"title":"Systems engineering and water resources management: A closer relationship is needed","authors":"Neil S. Grigg","doi":"10.1002/sys.21725","DOIUrl":"https://doi.org/10.1002/sys.21725","url":null,"abstract":"Abstract Systems engineering can be applied in a broad spectrum of sectors, but only its analysis tool has been applied in the field of water resources management. Because systems engineering has a separate community of practice from water resources, there is little crosstalk between the two fields. As a result, the systems engineering functions that support planning, design, production, procurement, and customer support are not being applied to water systems. Meanwhile, water systems exhibit complexities that have generated a separate field named Integrated Water Resources Management that continues to confuse its followers after several decades. Its methods are applied to a broad spectrum of water issues that affect multiple stakeholders with conflicting interests and involve distinct subsystems, such as water supply or hydropower, as well as combinations of them. Use of systems analysis for such water issues began six decades ago, but it is still a work in progress. Evolving methods of systems engineering offer new possibilities to address problems of water resources management, but they must extend beyond systems analysis, which belongs to multiple disciplines. Examples show possibilities to apply systems engineering methods when water issues exhibit attributes of engineered systems and do not involve social and environmental complexities that cannot be included in system boundaries. Collaboration among systems engineering and water resources management would offer a fertile test bed to advance both fields.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The implementation of Fourth Industrial Revolution technologies at a manufacturing site requires analysis of the site status based on real‐time information, optimization of the processes, and onsite execution. However, in labor‐intensive industries, such as the garment‐manufacturing industry, it is extremely difficult to develop smart factories based on real‐time onsite information because such industries are accustomed to managing labor using empirical expert judgment; moreover, they require rapid production circulation and are dependent on human‐related factors. In this study, we developed an optimization simulator using onsite real‐time production information that provides decision‐making support to maximize the productivity of a garment production plant. As an optimization method, a genetic algorithm was used to incorporate operator relocation into various garment production line variables. Through the developed simulator, field managers can predict and optimize productivity with simple operation in connection with production information. The application of the simulation optimization process to an actual production line in an Indonesian garment factory indicated that the simulator can improve productivity by 34.8%. The results of this study will provide guidance regarding the application of industrial information integration in labor‐intensive industries using methods that can systematically support decision‐making to achieve optimal productivity.
{"title":"Garment production line optimization using production information based on real‐time power monitoring data","authors":"Woo‐Kyun Jung, Younguk Song, Eun Suk Suh","doi":"10.1002/sys.21724","DOIUrl":"https://doi.org/10.1002/sys.21724","url":null,"abstract":"Abstract The implementation of Fourth Industrial Revolution technologies at a manufacturing site requires analysis of the site status based on real‐time information, optimization of the processes, and onsite execution. However, in labor‐intensive industries, such as the garment‐manufacturing industry, it is extremely difficult to develop smart factories based on real‐time onsite information because such industries are accustomed to managing labor using empirical expert judgment; moreover, they require rapid production circulation and are dependent on human‐related factors. In this study, we developed an optimization simulator using onsite real‐time production information that provides decision‐making support to maximize the productivity of a garment production plant. As an optimization method, a genetic algorithm was used to incorporate operator relocation into various garment production line variables. Through the developed simulator, field managers can predict and optimize productivity with simple operation in connection with production information. The application of the simulation optimization process to an actual production line in an Indonesian garment factory indicated that the simulator can improve productivity by 34.8%. The results of this study will provide guidance regarding the application of industrial information integration in labor‐intensive industries using methods that can systematically support decision‐making to achieve optimal productivity.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136315349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests.
{"title":"A methodology for parameter estimation in system dynamics models using artificial intelligence","authors":"Jyotirmay Gadewadikar, Jeremy Marshall","doi":"10.1002/sys.21718","DOIUrl":"https://doi.org/10.1002/sys.21718","url":null,"abstract":"Abstract Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135306525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Current swift technological advances are significantly impacting how organizations operate and services are provided. Even with the multiple benefits for organizations that undergo digital transformation, the majority of transformation initiatives fail due to the challenges that arise. A primary reason for these failures links back to the lack of effective governance framework to support effective digital transformation efforts. This paper proposes such a framework through a system of systems engineering approach to understand the various constituent systems involved in digital transformation efforts and their interactive and emergent behaviors. The application and usefulness of the framework were demonstrated as part of a digital transformation initiative in an Australian Large Government Agency and documented as a case study.
{"title":"System of systems engineering governance framework for digital transformation: A case study of an Australian large government agency","authors":"Samantha Papavasiliou, Alex Gorod, Carmen Reaiche","doi":"10.1002/sys.21719","DOIUrl":"https://doi.org/10.1002/sys.21719","url":null,"abstract":"Abstract Current swift technological advances are significantly impacting how organizations operate and services are provided. Even with the multiple benefits for organizations that undergo digital transformation, the majority of transformation initiatives fail due to the challenges that arise. A primary reason for these failures links back to the lack of effective governance framework to support effective digital transformation efforts. This paper proposes such a framework through a system of systems engineering approach to understand the various constituent systems involved in digital transformation efforts and their interactive and emergent behaviors. The application and usefulness of the framework were demonstrated as part of a digital transformation initiative in an Australian Large Government Agency and documented as a case study.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISO/IEC/IEEE 15288:2015 is one of the most fundamental systems engineering international standards. In this work, the major system lifecycle processes specified in 15288 and, equally importantly, the objects interacting through them, are modeled meticulously using OPM ISO 19450. The conceptual model, based on this standard's text, reflects the implied authors’ intent, bringing up ambiguities that arise from the informality of natural language text and reference to related figures. The resulting OPM model is an exact, formal, and detailed expression of the processes and related objects in the first part of 15288, making it machine interpretable. The gaps discovered during the modeling process are testimony to the value of the model‐based standards authoring approach and the centrality of a formal yet humanly accessible model as the underlying backbone of international standards and key technical documents in general.
ISO/IEC/IEEE 15288:2015是最基本的系统工程国际标准之一。在这项工作中，15288中指定的主要系统生命周期过程，以及同样重要的是，通过它们交互的对象，都是使用OPM ISO 19450精心建模的。基于本标准文本的概念模型反映了隐含作者的意图，导致自然语言文本的非正式性和对相关数字的引用产生歧义。由此产生的OPM模型是15288第一部分中过程和相关对象的精确、正式和详细表达，使其具有机器可解释性。建模过程中发现的差距证明了基于模型的标准编写方法的价值，以及作为国际标准和关键技术文件的基础支柱的正式但可供人类访问的模型的中心地位。
{"title":"Model‐based standards authoring: ISO 15288 as a case in point","authors":"D. Dori","doi":"10.1002/sys.21721","DOIUrl":"https://doi.org/10.1002/sys.21721","url":null,"abstract":"ISO/IEC/IEEE 15288:2015 is one of the most fundamental systems engineering international standards. In this work, the major system lifecycle processes specified in 15288 and, equally importantly, the objects interacting through them, are modeled meticulously using OPM ISO 19450. The conceptual model, based on this standard's text, reflects the implied authors’ intent, bringing up ambiguities that arise from the informality of natural language text and reference to related figures. The resulting OPM model is an exact, formal, and detailed expression of the processes and related objects in the first part of 15288, making it machine interpretable. The gaps discovered during the modeling process are testimony to the value of the model‐based standards authoring approach and the centrality of a formal yet humanly accessible model as the underlying backbone of international standards and key technical documents in general.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47435399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Military program management and system engineering re quire the expression of costs and delay trade‐off with respect to system architecture. If architecture frameworks (AF) such as NATO (NAF) were designed to fill this common need, their current state is essentially descriptive. As it turns out, building defense systems architectures using those frameworks in a properly anticipated cost/delay budget envelope would require to have all system engineering already solved, because the architecture frameworks are designed to provide an explicit representation of the operational domain that can be used in analysis, for articulation of issues and requirements, as support to planning, and as a means of solution design and validation, among other things. Thus Quality‐Resource‐Time optimality in a regularly evolving environment cannot be represented in acceptable delay without automated optimization assistance. Our contribution in this article explores coupling architecture framework with operation research (OR) models to enable computer assisted design and evaluation of heterogeneous views in NATO Architecture Framework (NAF). Our illustrative example is a Linear Programming based bridge between program management and system engineering to anticipate optimal trade‐offs. This article presents promising results, with which we hope to show how OR and AF will be indivisible in architecture evaluation process.
{"title":"Defense program quality‐cost‐delay optimization: architecture framework, a bridge between program management and system engineering","authors":"Lorraine Brisacier‐Porchon, Omar Hammami","doi":"10.1002/sys.21720","DOIUrl":"https://doi.org/10.1002/sys.21720","url":null,"abstract":"Military program management and system engineering re quire the expression of costs and delay trade‐off with respect to system architecture. If architecture frameworks (AF) such as NATO (NAF) were designed to fill this common need, their current state is essentially descriptive. As it turns out, building defense systems architectures using those frameworks in a properly anticipated cost/delay budget envelope would require to have all system engineering already solved, because the architecture frameworks are designed to provide an explicit representation of the operational domain that can be used in analysis, for articulation of issues and requirements, as support to planning, and as a means of solution design and validation, among other things. Thus Quality‐Resource‐Time optimality in a regularly evolving environment cannot be represented in acceptable delay without automated optimization assistance. Our contribution in this article explores coupling architecture framework with operation research (OR) models to enable computer assisted design and evaluation of heterogeneous views in NATO Architecture Framework (NAF). Our illustrative example is a Linear Programming based bridge between program management and system engineering to anticipate optimal trade‐offs. This article presents promising results, with which we hope to show how OR and AF will be indivisible in architecture evaluation process.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49256511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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