scholarly journals Early Stage Model Based System Design under Uncertainties

2019 ◽  
Vol 1 (1) ◽  
pp. 3621-3630
Author(s):  
Kazuya Oizumi ◽  
Akio Ito ◽  
Kazuhiro Aoyama
Keyword(s):  
Complex System ◽  
System Design ◽  
Systems Engineering ◽  
Process Model ◽  
Early Stage ◽  
Design Team ◽  
Support Design ◽  
Model Based ◽  
Design Process Model ◽  
High Level

AbstractSystem design at the early stage of design plays an important role in design process. Model based systems engineering is seen as a prominent approach for this challenge. System design can be explored by means of system simulation. However, as the system is a complex system, system model tends to have high level of abstraction. Therefore, the models cannot depict every details of the system, which makes optimization unreasonable.Furthermore, at the early stage of design, there are many uncertainties such as success of technological developments. By properly incorporating uncertain factors in system design, the system can be tolerant. Currently system design is conducted by experienced experts. However, for more complex system, it would be difficult to continue the current practice. Therefore, a method to support design team to make decision in system design is needed.This paper proposes a computational support for the system design. Design constraints, which seems the core information that design team wants at system design, are modeled. By visualizing constraints quantitatively and intuitively, the proposed method can support design team to conduct system design and design study.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
Systems Design ◽  
System Level ◽  
Worst Case ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

Frameworks for Model-Based Design of Enterprise Information Systems

2009 ◽  
pp. 326-356 ◽  
Author(s):  
Mara Nikolaidou ◽  
Nancy Alexopoulou
Keyword(s):  
Information Systems ◽  
System Design ◽  
Systems Engineering ◽  
System Architecture ◽  
Enterprise Architecture ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Model Based ◽  
Modern Enterprise ◽  
Design Tradeoffs

System design is an important phase of system engineering, determining system architecture to satisfy specific requirements. System design focuses on analyzing performance requirements, system modeling and prototyping, defining and optimizing system architecture, and studying system design tradeoffs and risks. Modern enterprise information systems (EIS) are distributed systems usually built on multitiered client server architectures, which can be modeled using well-known frameworks, such as Zachman enterprise architecture or open distributed processing reference model (RM-ODP). Both frameworks identify different system models, named views, corresponding to discrete stakeholder’s perspectives, specific viewpoints, and could serve as a basis for model-based system design. The main focus of this chapter is to explore the potential of model-based design for enterprise information systems (EIS). To this end, the basic requirements for model-based EIS design are identified, while three alternative approaches are discussed based on the above requirements, namely, rational unified process for systems engineering (RUP SE), UML4ODP and EIS design framework.

scholarly journals Problem-Solving Design-Platform Model Based on the Methodological Distinctiveness of Service Design

2019 ◽  
Vol 5 (4) ◽  
pp. 78 ◽  
Author(s):  
Jeon
Keyword(s):  
Problem Solving ◽  
Participatory Design ◽  
Process Model ◽  
Design Thinking ◽  
Service Design ◽  
Problem Definition ◽  
Cooperative Research ◽  
The Core ◽  
Model Based ◽  
Design Process Model

This study explores the differentiated properties of service design in the context of the final value pursued by this methodology, avoiding the interpretation of pending issues to which service design is applied. First, the following were identified as the core properties of service design, differentiated from other design methodologies: “Design Thinking”, a creative problem-solving process; “User Experience Value”, the pursued goal; “Participatory Design”, a practical research methodology; and “Interaction between Users and Providers”, the core research scope of pending issues. Second, the study proposed a six-step service design process model based on the interrelationships between these properties. The “problem recognition” step identified a decline in the quality of user experiences and forms a self-awareness of dissatisfaction. Next, the “problem understanding” step conducts multidisciplinary cooperative research on dissatisfaction. Subsequently, the “problem deduction” step determines users’ unsatisfied desires through visualization of the core pending issues, and the “problem definition” step performs creative conception activities with problem-solving approaches for the unsatisfied desires. Further, the “problem-solving” step develops service design models, and finally, the “problem-solving strategy check” step confirms the utility of the models in a real-world application.

scholarly journals MBSEsec: Model-Based Systems Engineering Method for Creating Secure Systems

2020 ◽  
Vol 10 (7) ◽  
pp. 2574 ◽  
Author(s):  
Donatas Mažeika ◽  
Rimantas Butleris
Keyword(s):  
Systems Engineering ◽  
Impact Analysis ◽  
Early Stage ◽  
System Development ◽  
Security Analysis ◽  
Security Requirements ◽  
Security Risks ◽  
Secure Systems ◽  
Model Based ◽  
Analysis Process

This paper presents how Model-Based System Engineering (MBSE) could be leveraged in order to mitigate security risks at an early stage of system development. Primarily, MBSE was used to manage complex engineering projects in terms of system requirements, design, analysis, verification, and validation activities, leaving security aspects aside. However, previous research showed that security requirements and risks could be tackled in the MBSE model, and powerful MBSE tools such as simulation, change impact analysis, automated document generation, validation, and verification could be successfully reused in the multidisciplinary field. This article analyzes various security-related techniques and then clarifies how these techniques can be represented in the Systems Modeling Language (SysML) model and then further exploited with MBSE tools. The paper introduces the MBSEsec method, which gives guidelines for the security analysis process, the SysML/UML-based security profile, and recommendations on what security technique is needed at each security process phase. The MBSEsec method was verified by creating an application case study that reflects real-world problems and running an experiment where systems and security engineers evaluated the feasibility of our approach.

Automatic Integration of System-Level Design and System Optimization Based on SysML

2012 ◽  
Vol 249-250 ◽  
pp. 1154-1159
Author(s):  
Yu Sheng Liu ◽  
Wen Qiang Yuan
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Optimization Model ◽  
System Modeling ◽  
System Optimization ◽  
System Level ◽  
Design Parameters ◽  
System Level Design ◽  
Model Based ◽  
Level Design

Model based systems engineering (MBSE) is becoming a promising approach for the system-level design of complex mechatronics. And several MBSE tools are developed to conduct system modeling. However, the system design cannot be optimized in current MBSE tools. In this study, an approach is presented to conduct the task. A set of optimization stereotype is defined at first which is used to formalize the optimization model based on the system design model. Then the design parameters and their relationships applied optimization stereotypes are extracted and transferred to construct the tool-dependent optimization model. Finally, the optimization model is solved and the results are given back and then modify the corresponding system model automatically. In this paper, MagicDraw is used to model the whole system whereas Matlab optimizer is used for optimization. The combustion engine is chosen as the example to illustrate the proposed approach.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2014 ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
System Level ◽  
Worst Case ◽  
Team Members ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed to reach and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

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