A value-of-information based approach to simulation model refinement

2008 ◽  
Vol 40 (3) ◽  
pp. 223-251 ◽  
Author(s):  
Jitesh H. Panchal ◽  
Christiaan J.J. Paredis ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Simulation Model ◽  
Value Of Information ◽  
Model Refinement ◽  
A Value

Simulation Model Refinement for Decision Making Via a Value-of-Information Based Metric

2006 ◽  
Author(s):  
Jitesh H. Panchal ◽  
Christiaan J. J. Paredis ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Value Of Information ◽  
Design Space ◽  
Simulation Models ◽  
Model Refinement ◽  
Physical Behavior ◽  
Additional Information ◽  
A Value ◽  
The Difference

Since no simulation model is perfect, any simulation model for modeling a system’s physical behavior can be refined further. Hence, the question faced by a designer is — “How much refinement of a simulation model is adequate for a particular design problem?” To answer this question, we present a value-of-information based approach for determining the appropriate extent of refinement of simulation models. The value of additional information obtained via refinement of simulation models is measured as the difference between the maximum payoff that could possibly be achieved throughout the design space and the minimum possible payoff at the point in the design space selected using the simple model. The approach is presented using two examples — design of a pressure vessel and the design of a material.

Evaluation of inspection features including exposure risk using a value of information analysis

2021 ◽  
Vol 38 (1) ◽  
pp. 36-58
Author(s):  
Domenic Di Francesco ◽  
Marios Chryssanthopoulos ◽  
Michael Havbro Faber ◽  
Ujjwal Bharadwaj
Keyword(s):  
Value Of Information ◽  
Information Analysis ◽  
Exposure Risk ◽  
Value Of Information Analysis ◽  
A Value

A value of information methodology for multiobjective decisions in quantitative set‐based design

2021 ◽  
Author(s):  
Nicholas J. Shallcross ◽  
Gregory S. Parnell ◽  
Ed Pohl ◽  
Simon R. Goerger
Keyword(s):  
Value Of Information ◽  
A Value ◽  
Set Based Design

Balancing risk and trust for strategic alliance formation decisions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zachary A. Collier ◽  
Matthew D. Wood ◽  
Dale A. Henderson
Keyword(s):  
Value Of Information ◽  
Strategic Alliance ◽  
Selection Process ◽  
Subjective Assessment ◽  
Quantitative Model ◽  
Information Gathering ◽  
Content Type ◽  
Alliance Partner ◽  
Analysis Process ◽  
A Value

PurposeTrust entails the assumption of risk by the trustor to the extent that the trustee may act in a manner unaligned with the trustor's interests. Before a strategic alliance is formed, each firm formulates a subjective assessment regarding whether the other firm will behave in a trustworthy manner and not act opportunistically. To inform this partner analysis and selection process, the authors leverage the concept of value of information to quantify the benefit of information gathering activities on the trustworthiness of a potential trustee.Design/methodology/approachIn this paper, the authors develop a decision model that explicitly operationalizes trust as the subjective probability that a trustee will act in a trustworthy manner. The authors integrate the concept of value of information related to information gathering activities, which would inform a trustor about a trustee's trustworthiness.FindingsTrust inherently involves some degree of risk, and the authors find that there is practical value in carrying out information gathering activities to facilitate the partner analysis process. The authors present a list of trustworthiness indicators, along with a scoring sheet, to facilitate learning more about a potential strategic alliance partner.Originality/valueThe need for a quantitative model that can support risk-based strategic alliance decision-making for partner analysis represents a research gap in the literature. The modeling of strategic alliance partner analysis decisions from a value of information (VOI) perspective adds a contribution to the trust literature.

Tiered Chemical Testing: A Value of Information Approach

Risk Analysis ◽  
2004 ◽  
Vol 24 (6) ◽  
pp. 1625-1639 ◽  
Author(s):  
Fumie Yokota ◽  
George Gray ◽  
James K. Hammitt ◽  
Kimberly M. Thompson
Keyword(s):  
Value Of Information ◽  
Information Approach ◽  
A Value ◽  
Chemical Testing

Managing Design Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling

2007 ◽  
Author(s):  
Jitesh H. Panchal ◽  
Christiaan J. J. Paredis ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Design Process ◽  
Value Of Information ◽  
Cooling System ◽  
Design Decision ◽  
Interaction Patterns ◽  
Functional Requirements ◽  
Design Processes ◽  
A Value ◽  
Complex Design ◽  
The Impact

Design processes for multiscale, multifunctional systems are inherently complex due to the interactions between scales, functional requirements, and the resulting design decisions. While complex design processes that consider all interactions lead to better designs; simpler design processes where some interactions are ignored are faster and resource efficient. In order to determine the right level of simplification of design processes, designers are faced with the following questions: a) how should complex design-processes be simplified without affecting the resulting product performance? and b) how can designers quantify and evaluate the appropriateness of different design process alternatives? In this paper, the first question is addressed by introducing a method for determining the appropriate level of simplification of design processes — specifically through decoupling of scales and decisions in a multiscale problem. The method is based on three constructs: interaction patterns to model design processes, intervals to model uncertainty resulting from decoupling of scales and decisions, and value of information based metrics to measure the impact of simplification on the final design outcome. The second question is addressed by introducing a value-of-information based metric called improvement potential for quantifying the appropriateness of design process alternatives from the standpoint of product design requirements. The metric embodies quantitatively the potential for improvement in the achievement of product requirements by adding more information for design decision making. The method is illustrated via a datacenter cooling system design example.

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