A Methodology to Manage System-level Uncertainty During Conceptual Design

2006 ◽  
Vol 128 (4) ◽  
pp. 959-968 ◽  
Author(s):  
Jay D. Martin ◽  
Timothy W. Simpson
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
Kriging Model ◽  
Satellite System ◽  
System Level ◽  
Computationally Efficient ◽  
Design Decisions ◽  
Current Design ◽  
The Impact ◽  
Level Performance

Current design decisions must be made while considering uncertainty in both models of the design and inputs to the design. In most cases, high fidelity models are used with the assumption that the resulting model uncertainties are insignificant to the decision making process. This paper presents a methodology for managing uncertainty during system-level conceptual design of complex multidisciplinary systems. This methodology is based upon quantifying the information available in a set of observations of computationally expensive subsystem models with more computationally efficient kriging models. By using kriging models, the computational expense of a Monte Carlo simulation to assess the impact of the sources of uncertainty on system-level performance parameters becomes tractable. The use of a kriging model as an approximation to an original computer model introduces model uncertainty, which is included as part of the methodology. The methodology is demonstrated as a decision-making tool for the design of a satellite system.

A Methodology to Manage Uncertainty During System-Level Conceptual Design

2005 ◽  
Author(s):  
Jay D. Martin ◽  
Timothy W. Simpson
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
Kriging Model ◽  
Satellite System ◽  
System Level ◽  
Computationally Efficient ◽  
Design Decisions ◽  
Current Design ◽  
The Impact ◽  
Level Performance

Current design decisions must be made while considering uncertainty in both models and inputs to the design. In most cases this uncertainty is ignored in the hope that it is not important to the decision making process. This paper presents a methodology for managing uncertainty during system-level conceptual design of complex multidisciplinary systems. The methodology is based upon quantifying the information available in computationally expensive subsystem models with more computationally efficient kriging models. By using kriging models, the computational expense of a Monte Carlo simulation to assess the impact of the sources of uncertainty on system-level performance parameters becomes tractable. The use of a kriging model as an approximation to an original computer model introduces model uncertainty, which is included as part of the methodology. The methodology is demonstrated as a decision making tool for the design of a satellite system.

scholarly journals Sustainable Investments in Responsible SMEs: That’s What’s Distinguish Government VCs from Private VCs

2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Jeaneth Johansson ◽  
Malin Malmström ◽  
Joakim Wincent
Keyword(s):  
Decision Making ◽  
Investment Decisions ◽  
Societal Impact ◽  
Venture Capitalists ◽  
Decision Making Processes ◽  
Micro Level ◽  
Sustainable Investments ◽  
Private Venture ◽  
The Impact ◽  
Level Performance

Researchers question the impact of governmental venture capitalists (GVC) compared to private venture capitalists (PVC), but we know little about why this difference occurs and if this criticism is justified. We observed a group of GVCs and developed a new model that describes the way that GVCs process signals pre- and post-decisions. Certain macro level factors severely undermine micro level performance, causing GVCs to financially underperform with respect to PVCs. This helped us to understand that GVCs do not make investment decisions in the same way as PVCs, and what undermines the performance of GVCs’ decision-making processes. The main goals of GVCs are to promote investments in responsible SMEs, mobilizing societal impact. We discuss that the criticism of GVC needs to be more nuanced, as they have a different role than PVC in the financial system as providers of sustainable investments in responsible SMEs.

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 Factors influencing general practitioners decisions to refer Paediatric patients to the emergency department: a systematic review and narrative synthesis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ciara Conlon ◽  
Emma Nicholson ◽  
Beatriz Rodríguez-Martin ◽  
Roisin O’Donovan ◽  
Aoife De Brún ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Emergency Department ◽  
Paediatric Patient ◽  
General Practitioners ◽  
Clinical Decision Making ◽  
System Level ◽  
Clinical Factors ◽  
Paediatric Patients ◽  
The Impact

Abstract Background Clinical guidelines are integral to a general practitioner’s decision to refer a paediatric patient to emergency care. The influence of non-clinical factors must also be considered. This review explores the non-clinical factors that may influence general practitioners (GPs) when deciding whether or not to refer a paediatric patient to the Emergency Department (ED). Methods A systematic review of peer-reviewed literature published from August 1980 to July 2019 was conducted to explore the non-clinical factors that influence GPs’ decision-making in referring paediatric patients to the emergency department. The results were synthesised using a narrative approach. Results Seven studies met the inclusion criteria. Non-clinical factors relating to patients, GPs and health systems influence GPs decision to refer children to the ED. GPs reported parents/ caregivers influence, including their perception of severity of child’s illness, parent’s request for onward referral and GPs’ appraisal of parents’ ability to cope. Socio-economic status, GPs’ aversion to risk and system level factors such as access to diagnostics and specialist services also influenced referral decisions. Conclusions A myriad of non-clinical factors influence GP referrals of children to the ED. Further research on the impact of non-clinical factors on clinical decision-making can help to elucidate patterns and trends of paediatric healthcare and identify areas for intervention to utilise resources efficiently and improve healthcare delivery.

Evaluation Method for Conceptual Design Scheme Based on Fuzzy Mathematics

2012 ◽  
Vol 591-593 ◽  
pp. 25-29
Author(s):  
Peng Fei Tian ◽  
Shi Yan ◽  
Bi Ru Li
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
New Product Development ◽  
Evaluation Method ◽  
Design Stage ◽  
Fuzzy Mathematics ◽  
Qualitative Information ◽  
Design Scheme ◽  
The Impact ◽  
Mathematics Method

Selecting the favorable conceptual design scheme is the first step to make a new product development (NPD) successfully. To guarantee reliability and rationality of decision-making about multiple design schemes in conceptual design stage under the impact of uncertainties and qualitative information, we have employed KJ method to cluster the evaluation factors into 5 clusters such as emotion, ergonomics, aesthetics, core technology, and impact; and fuzzy mathematics method to deal with uncertainties and qualitative information effectively. The weights of evaluation factors were calculated by analytical hierarchy process (AHP). Fuzzy mathematics method is the comprehensive evaluation method and quantitative analysis which based on the “maximum membership degree evaluation”. All design schemes are ranked and selected according to the multiple evaluation score of parts with their weights. Finally, a case study for decision-making is presented to demonstrate the application of the evaluation method.

A Risk-Informed Decision Making Methodology for Evaluating Failure Impact of Early System Designs

2008 ◽  
Author(s):  
Tolga Kurtoglu ◽  
Irem Y. Tumer
Keyword(s):  
Decision Making ◽  
System Development ◽  
Informed Decision ◽  
System Level ◽  
Analysis Tool ◽  
Informed Decision Making ◽  
Simulation Based ◽  
System Risk ◽  
Guide System ◽  
The Impact

In this paper, we introduce a new risk-informed decision-making methodology for use during early design of complex systems. The proposed approach is based on the notion that a failure happens when a functional element in the system does not perform its intended task. Accordingly, risk is defined depending on the role of functionality in accomplishing designed tasks. A simulation-based failure analysis tool is used to analyze functional failures and their impact on overall system functionality. The analysis results are then integrated into a decision-making framework that relates the impact of functional failures and their propagation to decision making in order to guide system level design decisions. With the help of the proposed methodology, a multitude of failure scenarios can be quickly analyzed to determine the effects of decisions on overall system risk. Using this decision-making approach, design teams can systematically explore risks and vulnerabilities during early, functional stage of system development prior to the selection of specific components. Application of the presented method to a reservoir system design demonstrates these capabilities.

On Using Kriging Models for Complex Design

2011 ◽  
Author(s):  
Jay D. Martin
Keyword(s):  
Complex Systems ◽  
Robust Design ◽  
Kriging Model ◽  
Response Surfaces ◽  
Optimal System ◽  
System Level ◽  
Design Teams ◽  
Complex Design ◽  
Level Performance ◽  
Estimate System

The design of most modern systems requires the tight integration of multiple disciplines. In practice, these multiple disciplines are often optimized independently, given only fixed values or targets for their interactions with other disciplines. The result is a system that may not represent the optimal system-level design. It may also not be a robust design in the sense that small changes in each subsystem’s performance may have a large impact on the system-level performance. The use of kriging models to represent the response surfaces of subsystems that are then combined to estimate system-level performance can be used as a method to provide collaboration between design teams. The difficulty with this method is the creation of the models given potentially large number of dimensions or observations. This paper presents a method to reduce the dimensionality of the input space for kriging models used for designing of complex systems. The input dimensionality of the kriging model is reduced to only includes the most important factors needed for the prediction of the observed output. A result of using these reduced dimensionality models is the need to no longer force interpolation of all of the observations used to create the models.

Mitigating ethical conflict and moral distress in the care of patients on ECMO: impact of an automatic ethics consultation protocol

2021 ◽  
pp. medethics-2020-106881
Author(s):  
M Jeanne Wirpsa ◽  
Louanne M Carabini ◽  
Kathy Johnson Neely ◽  
Camille Kroll ◽  
Lucia D Wocial
Keyword(s):  
Decision Making ◽  
Moral Distress ◽  
Ethics Consultation ◽  
Outcome Data ◽  
System Level ◽  
Ethical Challenges ◽  
Ethical Conflict ◽  
Novel Therapy ◽  
Intervention Protocol ◽  
The Impact

AimsThis study evaluates a protocol for early, routine ethics consultation (EC) for patients on extracorporeal membrane oxygenation (ECMO) to support decision-making in the context of clinical uncertainty with the aim of mitigating ethical conflict and moral distress.MethodsWe conducted a single-site qualitative analysis of EC documentation for all patients receiving ECMO support from 15 August 2018 to 15 May 2019 (n=68). Detailed analysis of 20 ethically complex cases with protracted ethics involvement identifies four key ethical domains: limits of prognostication, bridge to nowhere, burden of treatment and system-level concerns. There are three subthemes: relevant contextual factors, the role of EC and observed outcomes. Content analysis of transcripts from interviews with 20 members of the multidisciplinary ECMO team yields supplemental data on providers’ perceptions of the impact of the early intervention protocol.ResultsLimited outcome data for ECMO, unclear indications for withdrawal, adverse effects of treatment and an obligation to attend to programme metrics present significant ethical challenges in the care of this patient population. Upstream EC mitigates ethical conflict by setting clear expectations about ECMO as a time limited trial, promoting consistent messaging among multiple services and supporting surrogate decision-makers. When ECMO becomes a ‘bridge to nowhere’, EC facilitates decision-making that respects patient values yet successfully sets limits on non-beneficial use of this novel therapy.ConclusionData from this study support the conclusion that ECMO poses unique ethical challenges that necessitate a standardised protocol for early, routine EC—at least while this medical technology is in its nascent stages.

scholarly journals Drip and ship versus direct to endovascular thrombectomy: The impact of treatment times on transport decision-making

2018 ◽  
Vol 3 (2) ◽  
pp. 126-135 ◽  
Author(s):  
Jessalyn K Holodinsky ◽  
Alka B Patel ◽  
John Thornton ◽  
Noreen Kamal ◽  
Lauren R Jewett ◽  
...  
Keyword(s):  
Decision Making ◽  
Ischaemic Stroke ◽  
Distance Matrix ◽  
Geographic Area ◽  
System Level ◽  
Tertiary Referral Centre ◽  
Endovascular Thrombectomy ◽  
Patient Transport ◽  
First Case ◽  
The Impact

Introduction In ischaemic stroke care, fast reperfusion is essential for disability free survival. It is unknown if bypassing thrombolysis centres in favour of endovascular thrombectomy (mothership) outweighs transport to the nearest thrombolysis centre for alteplase and then transfer for endovascular thrombectomy (drip-and-ship). We use conditional probability modelling to determine the impact of treatment times on transport decision-making for acute ischaemic stroke. Materials and methods Probability of good outcome was modelled using a previously published framework, data from the Irish National Stroke Register, and an endovascular thrombectomy registry at a tertiary referral centre in Ireland. Ireland was divided into 139 regions, transport times between each region and hospital were estimated using Google’s Distance Matrix Application Program Interface. Results were mapped using ArcGIS 10.3. Results Using current treatment times, drip-and-ship rarely predicts best outcomes. However, if door to needle times are reduced to 30 min, drip-and-ship becomes more favourable; even more so if turnaround time (time from thrombolysis to departure for the endovascular thrombectomy centre) is also reduced. Reducing door to groin puncture times predicts better outcomes with the mothership model. Discussion This is the first case study modelling pre-hospital transport for ischaemic stroke utilising real treatment times in a defined geographic area. A moderate improvement in treatment times results in significant predicted changes to the optimisation of a national acute stroke patient transport strategy. Conclusions Modelling patient transport for system-level planning is sensitive to treatment times at both thrombolysis and thrombectomy centres and has important implications for the future planning of thrombectomy services.

scholarly journals Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
V. Buenestado ◽  
M. Toril ◽  
S. Luna-Ramírez ◽  
J. M. Ruiz-Avilés
Keyword(s):  
Base Station ◽  
System Level ◽  
Static System ◽  
Computationally Efficient ◽  
Transmit Power ◽  
Main Driver ◽  
Planning Methods ◽  
Planning Algorithm ◽  
A Cell ◽  
The Impact

A computationally efficient self-planning algorithm for adjusting base station transmit power in a LTE system on a cell-by-cell basis is presented. The aim of the algorithm is to improve the overall network spectral efficiency in the downlink by reducing the transmit power of specific cells to eliminate interference problems. The main driver of the algorithm is a new indicator that predicts the impact of changes in the transmit power of individual cells on the overall network Signal to Interference plus Noise Ratio (SINR) for the downlink. Algorithm assessment is carried out over a static system-level simulator implementing a live LTE network scenario. During assessment, the proposed algorithm is compared with a state-of-the-art self-planning algorithm based on the modification of antenna tilt angles. Results show that the proposed algorithm can improve both network coverage and capacity significantly compared to other automatic planning methods.

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