No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Model-Based System Architecting and Decision-Making
... System architecture modeling is utilized in various industries, including defense, aerospace, automotive, and telecommunications. It is specifically compatible with multifaceted systems with multiple subsystems and components, where it may assist in making sure that the architecture of the system is optimized and well-designed for reliability, performance, and maintainability [18]. MBSA may be applied using multiple frameworks and methods, each with strengths and weaknesses. ...
The quadrotor, a versatile aerial vehicle extensively utilized in the aerospace and
aircraft industries, plays a significant role in various applications. One crucial aspect of
aircraft operations involves visually inspecting the external surface to ensure
airworthiness and flight safety. However, manual inspection methods are prone to
errors and are time-consuming. To overcome these limitations, advanced technologies
such as commercial drones have emerged as potential solutions. Although commercial
drones are available for this purpose, meeting stakeholders' requirements within
specific contextual constraints remain challenging. Moreover, the complexity of product
design increases due to customer requirements and high expectations, necessitating
effective management strategies. To address these challenges, significant
modifications are needed for commercial drones. Model-Based Systems Engineering
(MBSE) methods have shown promise in tackling these complexities. However,
implementing MBSE successfully poses additional challenges due to a lack of proper
modeling methodologies and tools. As a result, using Arcadia as a baseline needs to
address the need for flexibility, high traceability, and well-organized interdisciplinary
interfaces.
Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.
We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.
The development of a concept for a system is a key step toward creating the system's architecture. Most previous concept development approaches focus on the procedures for the conceptual design activity—the sequence of activities and tasks. Our work is motivated by the desire to elaborate in details the notional content of a system concept and to provide the means of encoding and analyzing it in a digital environment. The objective of this work is to develop a system concept representation framework that can systematically represent the concept's constituents, their definitions, and interconnections. In order to demonstrate the utility of this framework, we have conducted three studies: mapped eight selected US patents, nine selected urban architectural patterns, and three selected software patterns to the framework. Patents, urban architectural patterns, and software patterns each contain a rich body of knowledge about the system they describe, and therefore they must logically contain a description of the concepts underlying them. We show that the main features of proposed framework can be found in patents, urban architectural patterns, and software patterns. The major utility of the framework is that it provides the means to encode existing system concepts and to inform the conceptual design of new systems, contributing to the INCOSE Model‐Based Conceptual Design initiative.
Remote-controlled or autonomous multi-rotor air vehicles, or drones, have become common and commercially available even to individual consumers, mostly for imaging purposes. Drones appeal to mission architects looking to extend the toolbox provided to operators performing challenging missions such as public safety operations. However, careful analysis of the operational context and concept of operations must take place before major acquisitions. The purpose of this paper is to propose a model-based operational architecture definition framework, which is based on the Department of Defense Architecture Framework (DoDAF) ontology and uses Object Process Methodology (OPM) as its underlying modeling language. Through careful mapping of DoDAF Operational Viewpoint (OV) ontology to OPM ontology, we were able to show that the entire OV ontology can be covered by a small set of objects, processes , relations among them, and constructs comprising them. We then show how to instantiate the ontology to create a model of an actual architecture of interest (AoI) while maintaining strong typing of the model elements to ensure validity, integrity, consistency, and continuous compliance with the OV. We demonstrate our approach on the case of using drones in public safety enterprises for the purpose of crowd management in massively attended events and locations. The proposed framework allows for capturing ConOps and OpsCon in a lightweight, yet robust and consistent manner, and improve communication and concept validation between operational stakeholders and enterprise architects.
This chapter presents Methodical Approach to Executable Integrated Modeling (MAXIM) and its implementation environment, OPCloud. The MAXIM framework enables concurrent modeling of the hardware and software system aspects, avoiding the need to make the painful and information‐leaking transition from the abstract, qualitative conceptual system architecting stage to the concrete, detailed, quantitative design stage. The MAXIM environment aims to overcome the widening hardware‐software modeling gap, stepping toward bringing systems engineering and software engineering closer together. OPCloud is revolutionary in that it is the first and only modeling environment that enables modeling systems not just conceptually; the same environment also provides the modeler with the ability to proceed with detailed, quantitative design that is integrated into the qualitative model. The chapter discusses the sharing of model data and describes collaboration facilities as built into OPCloud.
In this chapter we argue for the use of representations from category theory
to support better models for complex systems, and provide an example of such an
application might look like. Our approach rests on the well known observation that
complex system design is a fundamentally recursive discipline, which are formalized
in computer science using structures called algebras, coalgebras and operads,
mathematical structures which are intimately linked to labeled tree representations.
We then develop two small examples to demonstrate the utility of this approach.
The first defines a logical semantics of contracts to organize requirements at different
scales in hierarchical systems. The second concerns the integration of artificial
intelligence models into a preexisting human-driven process.
This textbook is about three key aspects of system design: decision making under uncertainty, trade-off studies and formal risk analyses. Recognizing that the mathematical treatment of these topics is similar, the authors generalize existing mathematical techniques to cover all three areas. Common to these topics are importance weights, combining functions, scoring functions, quantitative metrics, prioritization and sensitivity analyses. Furthermore, human decision-making activities and problems use these same tools. Therefore, these problems are also treated uniformly and modeled using prospect theory. Aimed at both engineering and business practitioners and students interested in systems engineering, risk analysis, operational management, and business process modeling, Tradeoff Decisions in System Design explains how humans can overcome cognitive biases and avoid mental errors when conducting trade-off studies and risk analyses in a wide range of domains. With generous use of examples as a common thread across chapters this book.
Co-design problems in the field of robotics involve the tradeoff of “resources” usage, such as cost, execution time, and energy, with mission performance, under recursive constraints that involve energetics, mechanics, computation, and communication. This letter shows that a large class of codesign problems have a common structure, as they are described by two posets, representing functionality, and resources. The codesign constraints can be expressed as two maps in opposite directions between the two posets. Finding the most resource-economical feasible solution is equivalent to finding the least fixed point of the composition of those two maps. If the two maps are monotone, results from order theory allow concluding uniqueness and systematically deriving an optimal design or a certificate for infeasibility.
System-based decision-making functionality has matured over the last decade due to the constant evolution of technological decision execution capabilities. Intelligent agents, cyber-physical systems employing automated and autonomous mechanisms and components, and recent breakthroughs of household robotics require standardized and formalized automated decision making (DM) process design. Employing an integrated systems thinking approach, we propose a model-based approach which integrates decision-support and decision-making mechanisms into system models. Our formal conceptual metamodel streamlines a standard, generic DM process, enabling the coordination and consolidation of local system-based decisions in a loosely coordinated system-of-systems or a complex system of autonomous agents into a coherent, environment-aware global decision. Our framework utilizes Object Process Methodology – OPM – a robust conceptual modeling framework for complex system and process modeling and design.
The sustainability of space exploration will depend in large part on its ability to consistently and reliably deliver valued benefits to societal stakeholders over an extended period. This on-going research studies the values of prospective stakeholders in the space exploration enterprise—both in the near term and with a perspective extending over decades. The immediate focus is human and robotic exploration of the Earth/Moon system, but extends to the exploration of Mars as well. Potential beneficiaries of space exploration are identified in broad societal sectors. An analysis of these stakeholders, their values and needs leads to the development of a comprehensive set of space exploration objectives that address those needs. The relative priority of exploration objectives is weighted using information about stakeholder characteristics, values, and their role and place in the exploration value stream. The weighted exploration objectives can then be used to assess the relative value of different technical system architectures, and to design exploration enterprise architecture, attributes and policy frameworks to enable value delivery to societal stakeholders. Ultimately, through stakeholders' continuing support, sustainable space exploration will be delivered.
The inability to approach systematically the high level of ambiguity present in the early design phases of space systems causes long, highly iterative, and costly design cycles. A process is introduced and described to capture decision maker preferences and use them to generate and evaluate a multitude of space system designs, while providing a common metric that can be easily communicated throughout the design enterprise. Communication channeled through formal utility interviews and analysis enables engineers to better understand the key drivers for the system and allows for a more thorough exploration of the design tradespace. Multi-attribute tradespace exploration with concurrent design, a process incorporating decision theory into model- and simulation-based design, has been applied to several space system projects at the Massachusetts Institute of Technology. Preliminary results indicate that this process can improve the quality of communication to resolve more quickly project ambiguity and to enable the engineer to discover better value designs for multiple stakeholders. The process is also integrated into a concurrent design environment to facilitate the transfer of knowledge of important drivers into higher fidelity design phases. Formal utility theory provides a mechanism to bridge the language barrier between experts of different backgrounds and differing needs, for example, scientists, engineers, managers, etc. Multi-attribute tradespace exploration with concurrent design couples decision makers more closely to the design and, most important, maintains their presence between formal reviews.
Requirements Development, Requirements Verification, Requirements Validation, System Verification, and System Validation are important systems engineering tasks. This paper describes these tasks and then discusses famous systems where these tasks were done correctly and incorrectly. This paper shows examples of the differences between developing requirements, verifying requirements, validating requirements, verifying a system, and validating a system. Understanding these differences may help increase the probability of success of future system designs. © 2004 Wiley Periodicals, Inc. Syst Eng 8: 1–14, 2005
An introduction to a powerful and flexible network modeling tool for developing and understanding complex systems, with many examples from a range of industries.
Design structure matrix (DSM) is a straightforward and flexible modeling technique that can be used for designing, developing, and managing complex systems. DSM offers network modeling tools that represent the elements of a system and their interactions, thereby highlighting the system's architecture (or designed structure). Its advantages include compact format, visual nature, intuitive representation, powerful analytical capacity, and flexibility. Used primarily so far in the area of engineering management, DSM is increasingly being applied to complex issues in health care management, financial systems, public policy, natural sciences, and social systems. This book offers a clear and concise explanation of DSM methods for practitioners and researchers.
A variety of researches are examined from the standpoint of information theory. It is shown that the unaided observer is severely limited in terms of the amount of information he can receive, process, and remember. However, it is shown that by the use of various techniques, e.g., use of several stimulus dimensions, recoding, and various mnemonic devices, this informational bottleneck can be broken. 20 references. (PsycINFO Database Record (c) 2006 APA, all rights reserved).
Early studies of intuitive judgment and decision making conducted with the late Amos Tversky are reviewed in the context of two related concepts: an analysis of accessibility, the ease with which thoughts come to mind; a distinction between effortless intuition and deliberate reasoning. Intuitive thoughts, like percepts, are highly accessible. Determinants and consequences of accessibility help explain the central results of prospect theory, framing effects, the heuristic process of attribute substitution, and the characteristic biases that result from the substitution of nonextensional for extensional attributes. Variations in the accessibility of rules explain the occasional corrections of intuitive judgments. The study of biases is compatible with a view of intuitive thinking and decision making as generally skilled and successful.
Model-Based Systems Engineering (MBSE), which tackles architecting and design of complex systems through the use of formal models, is emerging as the most critical component of systems engineering. This textbook specifies the two leading conceptual modeling languages, OPM-the new ISO 19450, composed primarily by the author of this book, and OMG SysML. It provides essential insights into a domain-independent, discipline-crossing methodology of developing or researching complex systems of any conceivable kind and size. Combining theory with a host of industrial, biological, and daily life examples, the book explains principles and provides guidelines for architecting complex, multidisciplinary systems, making it an indispensable resource for systems architects and designers, engineers of any discipline, executives at all levels, project managers, IT professional, systems scientists, and engineering students. © Springer Science+Business Media New York 2016. All rights reserved.
IntroductionMethodological ApproachDifferences Between Single- and Multiple-Objective Decision TreesSummaryExample Problems
In 1977, Nam P Suh proposed a different approach to design research. Suh’s approach was different in that it introduced the notions of domains and layers in a 2-D design thinking and stipulated a set of axioms that describes what is a good design. Following Suh’s 2-D reasoning structure in a zigzagging manner and applying these axioms through the design process should enable the designer to arrive at a good design. In this paper, we present our own experiences in applying Suh’s theories to software design, product design, organizational design, process design, and more in both academic and industrial settings. We also share our experience from teaching the Axiomatic Design theory to students at universities and engineers in industry, and draw conclusions on how best to teach and use this approach, and what results one can expect. The merits of the design axioms are discussed based on the practical experiences that the authors have had in their application. The process developed around the axioms to derive maximum value (solution neutral environment, design domains, what-how relationship, zig-zag process, decomposition, and design matrices) is also discussed and some updates are proposed.
Introduction Methodological Approach Differences Between Single- and Multiple-Objective Decision Trees Summary Example Problems References
Interviews with European and US software architects show not only a diverse practice of architecting but also the architect's transformation from primary decision maker to coordinator, advisor, and knowledge manager.
The INCOSE Model-Based Conceptual Design (MBCD) Working Group was established in 2011 with the purpose of advancing the body of knowledge and practice of systems engineering (SE), through the development and application of model-based systems engineering (MBSE) methodologies to the concept stage of a system's life-cycle. The Working Group strives to achieve its vision through a series of activities conducted by members of the working group. One of these activities was to conduct a survey of people involved in MBCD, with the goal: To identify the issues associated with performing Conceptual Design and areas where MBCD has been successful. As part of this activity, two surveys, one in 2014 and one during 2015, have been conducted. This paper begins with an overview of the first survey and the need for a second survey that arose from the possibility of non-response bias being present in the first survey's results. Following this, the results obtained from the second survey are presented, compared with the results from the first survey and discussed. The key insights the authors uncovered from the results, survey workshops, and symposium presentations on both surveys are also identified. The paper concludes with a discussion on how these results are being used to inform current MBCD WG activities and strategic planning.
With the increasing demands for affordable system capabilities that can be provided quickly to the user community, developers must explore a variety of options for identifying "satisficing" solutions. The system capability affordability tradespace must balance expedited systems engineering to reduce schedule and cost, encourage flexibility in architecture decisions to support future evolution of the system, and minimize technical debt that either results in later rework or adversely impacts future options. This paper shows how the University of Southern California (USC) Center for Systems and Software Engineering (CSSE) software and systems engineering cost models can be used in the analysis of this tradespace to show the range of options and the resulting consequences.
This paper serves as an introduction to the Analytic Hierarchy Process - A multicriteria decision making approach in which factors are arranged in a hierarchic structure. The principles and the philosophy of the theory are summarized giving general background information of the type of measurement utilized, its properties and applications.
Due to the complexity of contemporary technology, product and system
design efforts often require intensive organization and communication
within teams; the design venture must accordingly be carefully planned
and systematically executed, integrating the various aspects of the
design process into a logical and comprehensible whole. The present
comprehensive and systematic treatment of this methodology proceeds by
clarifying the design task, establishing the function structures of a
conceptual design, and finally determining the definitive layout
embodying the design. Illustrative examples of actual product design
processes and their results are presented and evaluated.
Bayesian rules for decision under uncertainty are derived constructively from two principles of consistent behavior and two principles asserting that the decision maker can scale his preferences for consequences and judgments concerning unpredictable events by reference to simple lotteries involving only two consequences and based on an imaginary experiment with subjectively equally likely outcomes. It is shown that the two principles of consistent behavior require the decision maker's scaled judgments to obey the axioms of probability, and by use of one further principle of consistent behavior it is shown that they should also agree with the usual definition of conditional probability and hence with Bayes' rule.
In response to the increasing criticality of software within systems and the increasing demands being put onto 21st century systems, systems and software engineering processes will evolve significantly over the next two decades. This paper identifies eight relatively surprise-free trends—the increasing interaction of software engineering and systems engineering; increased emphasis on users and end value; increased emphasis on systems and software dependability; increasingly rapid change; increasing global connectivity and need for systems to interoperate; increasingly complex systems of systems; increasing needs for COTS, reuse, and legacy systems and software integration; and computational plenty. It also identifies two “wild card” trends: increasing software autonomy and combinations of biology and computing. It then discusses the likely influences of these trends on systems and software engineering processes between now and 2025, and presents an emerging scalable spiral process model for coping with the resulting challenges and opportunities of developing 21st century software-intensive systems and systems of systems. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 1–19, 2006
Even when sophisticated software development process is used, e.g., the Stakeholder Win Win Risk-Driven Spiral Process, software development projects often fail. A prime reason for this failure is unresolved, often undetected, differences among stakeholders' sets of assumptions-their "models"-of various aspects of the project. MBASE is ail approach to identifying "model clashes" so that their risks can be dealt with. Although it was originally developed as ail extension to plan-driven development processes like the Stakeholder Will Win Risk-Driven Spiral Process, it call be adapted for use with any type of development process from Agile to Plan-driven. We discuss the notion of a "model," the various types of "model clashes" and their consequences, and the MBASE approach to dealing with them.
During conceptual design, engineers deal with incomplete product descriptions called design concepts. Engineers must compare these concepts in order to move towards the more desirable designs. However, comparisons are difficult because a single concept associates with numerous possible final design specifications, and any meaningful comparison of concepts must consider this range of possibilities. Consequently, the performance of a concept can only be characterized imprecisely. While standard multi- attribute utility theory is an accepted framework for making preference-based decisions between precisely characterized alternatives, it does not directly accommodate the analysis of imprecisely characterized alternatives. By extending uncertainty representations to model imprecision explicitly, it is possible to apply the principles of utility theory to such problems. However, this can lead to situations of indeterminacy, meaning that the decision maker is unable to identify a single concept as the most preferred. Under a set-based perspective and approach to design, a designer can work towards a single solution systematically despite indecision arising from imprecise characterizations of design concepts. Existing work in set-based design primarily focuses on feasibility conditions and single-attribute objectives, which are insufficient for most design problems. In this article, we combine the framework of multi-attribute utility theory, the perspective of set-based design, and the explicit mathematical representation of imprecision into a single approach to conceptual design. Each of the component theories are discussed, and their combined application developed. The approach is illustrated using the conceptual design of a fixed-ratio power transmission as an example. Additionally, important directions for future research are identified, with a particular focus on the process of modeling abstract design concepts.
This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness,
which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process
B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a
class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical
prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead
to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could
improve judgements and decisions in situations of uncertainty.
We investigate several conceptions of linguistic structure to determine whether or not they can provide simple and "revealing" grammars that generate all of the sentences of English and only these. We find that no finite-state Markov process that produces symbols with transition from state to state can serve as an English grammar. Furthermore, the particular subclass of such processes that produce n -order statistical approximations to English do not come closer, with increasing n , to matching the output of an English grammar. We formalize-the notions of "phrase structure" and show that this gives us a method for describing language which is essentially more powerful, though still representable as a rather elementary type of finite-state process. Nevertheless, it is successful only when limited to a small subset of simple sentences. We study the formal properties of a set of grammatical transformations that carry sentences with phrase structure into new sentences with derived phrase structure, showing that transformational grammars are processes of the same elementary type as phrase-structure grammars; that the grammar of English is materially simplified if phrase structure description is limited to a kernel of simple sentences from which all other sentences are constructed by repeated transformations; and that this view of linguistic structure gives a certain insight into the use and understanding of language.
Decision analysis has emerged from theory to practice to form a discipline for balancing the many factors that bear upon a decision. Unusual features of the discipline are the treatment of uncertainty through subjective probability and of attitude toward risk through utility theory. Capturing the structure of problem relationships occupies a central position; the process can be visualized in a graphical problem space. These features are combined with other preference measures to produce a useful conceptual model for analyzing decisions, the decision analysis cycle. In its three phases¿deterministic, probabilistic, and informational¿the cycle progressively determines the importance of variables in deterministic, probabilistic, and economic environments. The ability to assign an economic value to the complete or partial elimination of uncertainty through experimentation is a particularly important characteristic. Recent applications in business and government indicate that the increased logical scope afforded by decision analysis offers new opportunities for rationality to those who wish it.
Benchmarking the Benefits and Current Maturity of Model-Based Systems
- T A Mcdermott
- N Hutchinson
- M Clifford
- E Van Aken
- A Slado
- K Henderson
- TA McDermott
Guidelines for Model-Based (System) Architecting and Software Engineering (MBASE)
- B Boehm
- D Klappholz
- E Colbert
- P Puri
- A Jain
- J Bhuta
- H Kitapci
Architecting: How much and when?
- B Boehm
- A Oram
- G Wilson
Model Based System Architecting Reference Model
- Y Menshenin
- Y Mordecai
INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, Fourth Edi
- Incose
The principles of design
- N P Suh
- NP Suh
Systems Engineering Handbook, v. 3.2.2. International Council on Systems Engineering
- C Haskins
- K Forsberg
- M Krueger
- D Walden
- R D Hamelin
The Art of Systems Architecting, Second Edi
- M W Maier
- E Rechtin