Target Management in Complex System Design Using System Norms

2004 ◽  
Vol 127 (4) ◽  
pp. 536-544 ◽  
Author(s):  
H. Mahmoud ◽  
P. Kabamba ◽  
A. G. Ulsoy ◽  
G. Brusher
Keyword(s):  
Complex System ◽  
System Design ◽  
Output Signal ◽  
System Performance ◽  
Mimo Systems ◽  
System Level ◽  
Engineering System ◽  
Subsystem Design ◽  
Target Management ◽  
Complex System Design

The problem of setting, balancing, and determining priorities of design targets among the subsystems constituting an engineering system, i.e., managing the targets, is addressed. A new norm-based benchmarking approach is proposed to relate the system-level design objectives to subsystem design targets. The proposed approach provides a systematic means of setting and balancing subsystem design targets to deliver the desired system performance and ranks the priorities of the subsystem targets. Furthermore, the use of system norms, rather than output signal norms, to quantify system and subsystem performance reduces the number of design targets in multi-input multi-output (MIMO) systems. The approach is illustrated on a vehicle example, consisting of a frame, body, and body mounts as the subsystems.

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.

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.

scholarly journals Changing Subsystem Information Strategies Using Weighted Objectives: Increasing Robustness to Biased Information Passing

2015 ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Field Research ◽  
Local Optimization ◽  
System Level ◽  
Incentive Structure ◽  
Information Strategies ◽  
System Robustness ◽  
Complex System Design ◽  
Biased Information

Complex system design requires managing competing objectives between many subsystems. Previous field research has demonstrated that subsystem designers may use biased information passing as a negotiation tactic and thereby reach sub-optimal system-level results due to local optimization behavior. One strategy to combat the focus on local optimization is an incentive structure that promotes system-level optimization. This paper presents a new subsystem incentive structure based on Multi-disciplinary Optimization (MDO) techniques for improving robustness of the design process to such biased information passing strategies. Results from simulations of different utility functions for a test suite of multi-objective problems quantify the system robustness to biased information passing strategies. Results show that incentivizing subsystems with this new weighted structure may decrease the error resulting from biased information passing.

Capturing Interactions and Emergent Failure Behavior in Complex Engineered Systems at Multiple Scales

2011 ◽  
Author(s):  
Nikolaos Papakonstantinou ◽  
Seppo Sierla ◽  
David C. Jensen ◽  
Irem Y. Tumer
Keyword(s):  
Complex System ◽  
System Design ◽  
Emergent Behavior ◽  
System Level ◽  
Design Stage ◽  
Failure Behavior ◽  
Early Design ◽  
Large Complex ◽  
Large Complex System ◽  
Complex System Design

Large complex systems exhibit complex nominal and failure behavior and understanding that behavior is critical to the accurate assessment of risk. However, this assessment is difficult to accomplish in the early design stage. Multiple subsystem interactions and emergent behavior further complicate early design risk analysis. The goal of this paper is to demonstrate necessary modifications of an existing function-based failure assessment tool for application to the large complex system design domain. Specifically, this paper demonstrates how specific adaptations to this early, qualitative approach to system behavioral simulation and analysis help overcome some of the challenges to large complex system design. In this paper, a boiling water nuclear reactor design serves as a motivating case study for showing how this approach can capture complex subsystem interactions, identify emergent behavior trends, and assess failures at both the component and system level.

scholarly journals A Study of Student Design Team Behaviors in Complex System Design

2012 ◽  
Vol 134 (12) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
System Integration ◽  
Large Scale ◽  
Engineering Students ◽  
System Level ◽  
Design Parameters ◽  
Local Searches ◽  
Wide Range ◽  
Complex System Design

Large-scale engineering systems require design teams to balance complex sets of considerations using a wide range of design and decision-making skills. Formal, computational approaches for optimizing complex systems offer strategies for arriving at optimal solutions in situations where system integration and design optimization are well-formulated. However, observation of design practice suggests engineers may be poorly prepared for this type of design. Four graduate student teams completed a distributed, complex system design task. Analysis of the teams' design histories suggests three categories of suboptimal approaches: global rather than local searches, optimizing individual design parameters separately, and sequential rather than concurrent optimization strategies. Teams focused strongly on individual subsystems rather than system-level optimization, and did not use the provided system gradient indicator to understand how changes in individual subsystems impacted the overall system. This suggests the need for curriculum to teach engineering students how to appropriately integrate systems as a whole.

Towards Risk as a Tradeable Parameter in Complex System Design Trades

2010 ◽  
Author(s):  
Douglas L. Van Bossuyt ◽  
Stephen D. Wall ◽  
Irem Y. Tumer
Keyword(s):  
Complex System ◽  
Conceptual Design ◽  
System Level ◽  
Risk Models ◽  
Conceptual Designs ◽  
The Creation ◽  
Risk Visualization ◽  
Visualization Techniques ◽  
Complex System Design ◽  
Important System

Complex system conceptual design trade studies traditionally consider risk after a conceptual design has been created. Further, one person is often tasked with collecting risk information and managing it from each subsystem. This paper proposes a method to explicitly consider and trade risk on the same level as other important system-level variables during the creation of conceptual designs in trade studies. The proposed risk trading method advocates putting each subsystem engineer in control of risk for each subsystem. A risk vector is proposed that organizes many different risk metrics for communication between subsystems. A method of coupling risk models to dynamic subsystem models is presented. Several risk visualization techniques are discussed. An example is presented based upon a simplified spacecraft model. The risk trading method discussed offers an approach to more thoroughly consider risk during the creation of conceptual designs in trade studies.

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