Computationally Efficient Imprecise Uncertainty Propagation in Engineering Design and Decision Making

2012 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Andrew Olewnik
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Computational Efficiency ◽  
Design Problem ◽  
Uncertainty Propagation ◽  
Future Research ◽  
Parameter Estimates ◽  
Design Decision ◽  
Computationally Efficient ◽  
Imprecise Uncertainty

Modeling uncertainty through probabilistic representation in engineering design is common and important to decision making that considers risk. However, representations of uncertainty often ignore elements of “imprecision” that may limit the robustness of decisions. Further, current approaches that incorporate imprecision suffer from computational expense and relatively high solution error. This work presents the Computationally Efficient Imprecise Uncertainty Propagation (CEIUP) method which draws on existing approaches for propagation of imprecision and integrates sparse grid numerical integration to provide computational efficiency and low solution error for uncertainty propagation. The first part of the paper details the methodology and demonstrates improvements in both computational efficiency and solution accuracy as compared to the Optimized Parameter Sampling (OPS) approach for a set of numerical case studies. The second half of the paper is focused on estimation of non-dominated design parameter spaces using decision policies of Interval Dominance and Maximality Criterion in the context of set-based sequential design-decision making. A gear box design problem is presented and compared with OPS, demonstrating that CEIUP provides improved estimates of the non-dominated parameter range for satisfactory performance with faster solution times. Parameter estimates obtained for different risk attitudes are presented and analyzed from the perspective of Choice Theory leading to questions for future research. The paper concludes with an overview of design problem scenarios in which CEIUP is the preferred method and offers opportunities for extending the method.

Preliminary Design Support System Based on a Generic Under-Constraint Solving Technique

2000 ◽  
Author(s):  
Hiroyuki Sawada ◽  
Xiu-Tian Yan
Keyword(s):  
Decision Making ◽  
Product Design ◽  
Engineering Design ◽  
Design Problem ◽  
Design Parameters ◽  
Problem Solver ◽  
Decision Making Process ◽  
Design Decision ◽  
Novel Approach ◽  
Design Support System

Abstract Engineering design is an intensive decision making process. A designer with an informative and insightful decision making support can usually produce high quality product design solutions with less or no rework. However, with current support designers very often face challenge or even difficulties as more and more design parameters come into design decision making process when a design progresses. This paper proposes a novel approach to providing designers with such a decision support by using under-constraint design problem solver. It is argued that design requirements represented in the form of Product Design Specifications (PDSs) can be converted into a set of related constraint expressions. These PDS constraint sets, which are usually incomplete, i.e., under-constrained, can then be solved by the solver to provide a designer with guided solutions for each design parameter, thus support a designer to make an informative and insightful design decision. A case study is finally presented in the paper to demonstrate how this approach is used to solve a real engineering design problem — a robotic finger system design.

A Context-Based Approach to Engineering Design Negotiation

2005 ◽  
Author(s):  
Mathieu Geslin ◽  
Yan Jin
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Collaborative Design ◽  
Large Scale ◽  
Research Direction ◽  
Distributed Artificial Intelligence ◽  
Future Research ◽  
Design Decision ◽  
Context Model ◽  
Engineering Design Problems

Complex and large-scale engineering design problems require a collaborative approach in order to be completed in a timely manner. Designers involved in such work are making collaborative design decision, and often have to negotiate to address intricate problems and resolve their discrepancies while exploring the design space, generating new ideas and compromising for agreement. Advances in negotiation research have been made in social psychology, distributed artificial intelligence, and decision theory, but few have been applied to design. We advocate that design context information is of paramount importance in the decision-making process. In this paper, an argumentation-based negotiation model is introduced to support collaborative design decision-making. This model relies on clear design context model, argument model, negotiation protocol and strategies. In this paper, we successively detail each of these components and conclude with a discussion on a real-world case example and our future research direction.

Computationally Efficient Imprecise Uncertainty Propagation

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Andrew Olewnik
Keyword(s):  
Case Studies ◽  
Computational Efficiency ◽  
Uncertainty Propagation ◽  
Probabilistic Representation ◽  
Computationally Efficient ◽  
Problem Dimension ◽  
Performance Problem ◽  
Solution Accuracy ◽  
Variable Interaction ◽  
Imprecise Uncertainty

Modeling uncertainty through probabilistic representation in engineering design is common and important to decision making that considers risk. However, representations of uncertainty often ignore elements of “imprecision” that may limit the robustness of decisions. Furthermore, current approaches that incorporate imprecision suffer from computational expense and relatively high solution error. This work presents a method that allows imprecision to be incorporated into design scenarios while providing computational efficiency and low solution error for uncertainty propagation. The work draws on an existing method for representing imprecision and integrates methods for sparse grid numerical integration, resulting in the computationally efficient imprecise uncertainty propagation (CEIUP) method. This paper presents details of the method and demonstrates the effectiveness on both numerical case studies, and a thermocouple performance problem found in the literature. Results for the numerical case studies, in most cases, demonstrate improvements in both computational efficiency and solution accuracy for varying problem dimension and variable interaction when compared to optimized parameter sampling (OPS). For the thermocouple problem, similar behavior is observed when compared to OPS. The paper concludes with an overview of design problem scenarios in which CEIUP is the preferred method and offers opportunities for extending the method.

Performance Estimation and Robust Design Decisions

2000 ◽  
Author(s):  
Xiao Tang ◽  
Sundar Krishnamurty
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Robust Design ◽  
Estimation Method ◽  
Performance Estimation ◽  
Accurate Estimation ◽  
Design Decision ◽  
Computationally Efficient ◽  
Design Variables ◽  
Highly Nonlinear

Abstract This paper deals with two major issues critical to the development and implementation of a decision-based robust design, namely, representation of design performance under conditions of uncertainty and the development of a robust design decision model. Specifically, this paper presents a computationally efficient procedure for accurate estimation of performance variance using a novel Surround Point Method (SPM) and discusses its incorporation into a decision-based robust design framework. Results indicate that by mimicking effects from Monte-Carlo Simulation (MCS), SPM-based uncertainty estimation method appears to offer the best promise in achieving an optimal balance between computational complexity and design-scenario independence. It can be expected to be a viable and applicable probability estimation tool in generic engineering design, and particularly useful in highly nonlinear configuration design with many design variables. Furthermore, to explicitly incorporate robustness criteria, this paper introduces the concept of design evaluation level as a means for decision-making in an evolving design process. Using this concept, this paper introduces a robust decision-based design methodology that can methodically handle multiple performance attributes, system constraints, and robustness issues in engineering design. These issues are discussed in the context of engineering design decision-making with the aid of a simple case study and the results are discussed.

Arrow's Theorem and Engineering Design Decision Making

1999 ◽  
Vol 11 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Michael J. Scott ◽  
Erik K. Antonsson
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Design Decision ◽  
Arrow’S Theorem ◽  
Arrow's Theorem

A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures During Visual Trade Space Exploration

2009 ◽  
Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Data Visualization ◽  
Space Exploration ◽  
Computational Thinking ◽  
Test Group ◽  
Design Decision ◽  
Ongoing Research ◽  
Trade Space Exploration ◽  
Insight Into

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.

Manufacturing Investment and Allocation in Product Line Design Decision-Making

2005 ◽  
Author(s):  
Jeremy J. Michalek ◽  
Oben Ceryan ◽  
Panos Y. Papalambros ◽  
Yoram Koren
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Product Line ◽  
Manufacturing Cost ◽  
Design Decision ◽  
Product Line Design ◽  
Product Decision ◽  
Market Consequences ◽  
Line Design ◽  
Is Design

An important aspect of product development is design for manufacturability (DFM) analysis that aims to incorporate manufacturing requirements into early product decision-making. Existing methods in DFM seldom quantify explicitly the tradeoffs between revenues and costs generated by making design choices that may be desirable in the market but costly to manufacture. This paper builds upon previous work coordinating models for engineering design and marketing product line decision-making by incorporating quantitative models of manufacturing investment and production allocation. The result is a methodology that considers engineering design decisions quantitatively in the context of manufacturing and market consequences in order to resolve tradeoffs, not only among performance objectives, but also between market preferences and manufacturing cost.

Imprecision in Engineering Design

1995 ◽  
Vol 117 (B) ◽  
pp. 25-32 ◽  
Author(s):  
E. K. Antonsson ◽  
K. N. Otto
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Engineering Design ◽  
Cost Estimation ◽  
Formal Method ◽  
Concurrent Design ◽  
Design Decision ◽  
Design Cost ◽  
Formal Information

Methods for incorporating imprecision in engineering design decision-making are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate set-based concurrent design.

Understanding the Utilization of Information Stimuli in Design Decision Making Using Eye Gaze Data

2015 ◽  
Author(s):  
Youyi Bi ◽  
Murtuza Shergadwala ◽  
Tahira Reid ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Eye Gaze ◽  
Design Decision ◽  
Design Engineers ◽  
Cognitive Decision ◽  
Improved Design ◽  
Student Subjects ◽  
Types Of Information ◽  
Normative Models

Research on decision making in engineering design has focused primarily on how to make decisions using normative models given certain information. However, there exists a research gap on how diverse information stimuli are combined by designers in decision making. In this paper, we address the following question: how do designers weigh different information stimuli to make decisions in engineering design contexts? The answer to this question can provide insights on diverse cognitive models for decision making used by different individuals. We investigate the information gathering behavior of individuals using eye gaze data from a simulated engineering design task. The task involves optimizing an unknown function using an interface which provides two types of information stimuli, including a graph and a list area. These correspond to the graphical stimulus and numerical stimulus, respectively. The study was carried out using a set of student subjects. The results suggest that individuals weigh different forms of information stimuli differently. It is observed that graphical information stimulus assists the participants in optimizing the function with a higher accuracy. This study contributes to our understanding of how diverse information stimuli are utilized by design engineers to make decisions. The improved understanding of cognitive decision making models would also aid in improved design of decision support tools.

An Evaluation of Operating Room Safety and Efficiency: Pilot Utilization of a Structured Focus Group Format and Three-Dimensional Video Mock-Up to Inform Design Decision Making

2011 ◽  
Vol 5 (1) ◽  
pp. 6-22 ◽  
Author(s):  
Nicholas Watkins ◽  
Mark Kobelja ◽  
Erin Peavey ◽  
Stephen Thomas ◽  
John Lyon
Keyword(s):  
Decision Making ◽  
Mixed Methods ◽  
Focus Group ◽  
Operating Room ◽  
Three Dimensional ◽  
Information Modeling ◽  
Stepwise Multiple Regression ◽  
Future Research ◽  
Design Decision ◽  
Group Format

Objective: The purpose of this investigation was to identify safety and efficiency-related design features for inclusion in operating room (OR) construction documents. Background: Organizations are confronted with an array of challenges when planning an OR, including inefficiencies in operations, adverse events, and a variety of innovations to choose from. Currently, techniques that can be used in design practice and to inform design decision making for implementable OR solutions are limited. Methods: The project team used a structured focus group format with mixed methods to solicit 19 varying surgical team members' reactions to a three-dimensional video mock-up of a proposed OR. Data from the 19 participants were analyzed using stepwise multiple regression and content analysis of open-ended responses. Results and Discussion: Results demonstrate that several features of the proposed OR design predict meaningful outcomes, including flexibility and satisfaction with the OR setup, adverse event prevention, team performance, and distractions and interruptions. Participants' suggested solutions include universal booms to support anesthetic and perfusion capabilities, a fixed circulating nursing workstation that faces the patient and is at the foot of the operating room table, a wall-mounted monitor across from the surgeon, and wiring to support a touch-screen control arm in OR surgical fields. Conclusions: Findings from structured focus groups with mixed methods lead to implementable design solutions for construction documentation. The expeditious qualities and objectivity of the format are value-adds to the design decision-making process. Future research should use various techniques such as virtual technologies and building information modeling.

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