A Framework for Flexible Systems and Its Implementation in Multiattribute Decision Making

2003 ◽  
Vol 126 (3) ◽  
pp. 412-419 ◽  
Author(s):  
Andrew Olewnik ◽  
Trevor Brauen ◽  
Scott Ferguson ◽  
Kemper Lewis
Keyword(s):  
Complex System ◽  
Decision Making ◽  
System Design ◽  
Design Methods ◽  
Flexible Systems ◽  
Flexible System ◽  
Starting Point ◽  
Multiple Tasks ◽  
Complex System Design

In this paper, a framework for the concept of flexibility in complex system design is presented. This is one of the first of many steps toward developing new design methods for designers that will aid them in the development of customizable systems that meet the requirements of multiple customers and multiple tasks. The hope is that this paper will provide both a starting point from which academia and industry can move forward in developing new design methods for flexible systems and a basis for establishing a standard lexicon for use when referring to flexible system design.

A Framework for Flexible Systems and its Implementation in Multiattribute Decision Making

2001 ◽  
Author(s):  
Andrew Olewnik ◽  
Trevor Brauen ◽  
Scott Ferguson ◽  
Kemper Lewis
Keyword(s):  
Complex System ◽  
Decision Making ◽  
System Design ◽  
Design Methods ◽  
Flexible Systems ◽  
Flexible System ◽  
Starting Point ◽  
Multiple Tasks ◽  
Complex System Design

Abstract In this paper, we present a framework for the concept of flexibility in complex system design. This is one of the first of many steps toward developing new design methods for designers that will aid them in development of customizable systems that meet the requirements of multiple customers and multiple tasks. The hope is that this paper will provide both a starting point from which academia and industry can move forward in developing new design methods for flexible systems and a basis for establishing a standard lexicon for use when referring to flexible system design.

Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Joseph R. Piacenza ◽  
Kenneth John Faller ◽  
Mir Abbas Bozorgirad ◽  
Eduardo Cotilla-Sanchez ◽  
Christopher Hoyle ◽  
...  
Keyword(s):  
Complex System ◽  
Decision Making ◽  
System Design ◽  
Robust Design ◽  
System Performance ◽  
System Level ◽  
Individual Risk ◽  
Cascading Failure ◽  
The Impact ◽  
Complex System Design

Abstract Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

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.

Toward a Functional Failure Modeling Method of Representing Prognostic Systems During the Early Phases of Design

2015 ◽  
Author(s):  
Caitlin Stack ◽  
Douglas L. Van Bossuyt
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
System Design ◽  
Power Plants ◽  
Pressurized Water ◽  
Functional Failure ◽  
Early Stages ◽  
Failure Modeling ◽  
Complex System Design ◽  
Early Phases

Current methods of functional failure risk analysis do not facilitate explicit modeling of systems equipped with Prognostics and Health Management (PHM) hardware. As PHM systems continue to grow in application and popularity within major complex systems industries (e.g. aerospace, automotive, civilian nuclear power plants), implementation of PHM modeling within the functional failure modeling methodologies will become useful for the early phases of complex system design and for analysis of existing complex systems. Functional failure modeling methods have been developed in recent years to assess risk in the early phases of complex system design. However, the methods of functional modeling have yet to include an explicit method for analyzing the effects of PHM systems on system failure probabilities. It is common practice within the systems health monitoring industry to design the PHM subsystems during the later stages of system design — typically after most major system architecture decisions have been made. This practice lends itself to the omission of considering PHM effects on the system during the early stages of design. This paper proposes a new method for analyzing PHM subsystems’ contribution to risk reduction in the early stages of complex system design. The Prognostic Systems Variable Configuration Comparison (PSVCC) eight-step method developed here expands upon existing methods of functional failure modeling by explicitly representing PHM subsystems. A generic pressurized water nuclear reactor primary coolant loop system is presented as a case study to illustrate the proposed method. The success of the proposed method promises more accurate modeling of complex systems equipped with PHM subsystems in the early phases of design.

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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