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.

Preference Construction, Sequential Decision Making, and Trade Space Exploration

2013 ◽  
Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Lorri A. Bennett ◽  
Sara E. Lego ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Design Decision ◽  
Sequential Process ◽  
Sequential Decision ◽  
Ongoing Research ◽  
Preference Construction ◽  
Sequential Decision Process ◽  
Preliminary Model ◽  
Trade Space Exploration

This paper develops and explores the interface between two related concepts in design decision making. First, design decision making is a process of simultaneously constructing one’s preferences while satisfying them. Second, design using computational models (e.g., simulation-based design and model-based design) is a sequential process that starts with low fidelity models for initial trades and progresses through models of increasing detail. Thus, decision making during design should be treated as a sequential decision process rather than as a single decision problem. This premise is supported by research from the domains of behavioral economics, psychology, judgment and decision making, neuroeconomics, marketing, and engineering design as reviewed herein. The premise is also substantiated by our own experience in conducting trade studies for numerous customers across engineering domains. The paper surveys the pertinent literature, presents supporting case studies and identifies use cases from our experiences, synthesizes a preliminary model of the sequential process, presents ongoing research in this area, and provides suggestions for future efforts.

Toward a Value-Driven Design Approach for Complex Engineered Systems Using Trade Space Exploration Tools

2014 ◽  
Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Gary Stump ◽  
Bryan L. Mesmer ◽  
...  
Keyword(s):  
Data Visualization ◽  
Space Exploration ◽  
Systems Design ◽  
Design Decision ◽  
Value Functions ◽  
Complex Engineered Systems ◽  
A Value ◽  
Trade Offs ◽  
Engineered Systems ◽  
Trade Space Exploration

Design decision-making involves trade-offs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision-maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multi-dimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems. In this paper, we investigate the use of TSE tools to support decision-makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of trade space exploration. In this paper, we investigate the different uses of trade space exploration in a VDD context. We map a traditional TSE process into a value-based trade environment to provide greater decision support to a design team during complex systems design. The research leverages existing TSE paradigms and multi-dimensional data visualization tools to identify optimal designs using a value function for a system. The feasibility of using these TSE tools to help formulate value functions is also explored. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multi-objective approach to capture the Pareto frontier. Ongoing and future work is also 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

The Importance of Training for Interactive Trade Space Exploration: A Study of Novice and Expert Users

2011 ◽  
Vol 11 (3) ◽  
Author(s):  
David Wolf ◽  
Jennifer Hyland ◽  
Timothy W. Simpson ◽  
Xiaolong (Luke) Zhang
Keyword(s):  
Space Exploration ◽  
Design Decision ◽  
Support Design ◽  
Computing Power ◽  
Training Video ◽  
Engine Combustion ◽  
Interactive Data ◽  
Effectiveness And Efficiency ◽  
Trade Space Exploration ◽  
Future Work

Thanks to recent advances in computing power and speed, engineers can now generate a wealth of data on demand to support design decision-making. These advances have enabled new approaches to search multidimensional trade spaces through interactive data visualization and exploration. In this paper, we investigate the effectiveness and efficiency of interactive trade space exploration strategies by conducting human subject experiments with novice and expert users. A single objective, constrained design optimization problem involving the sizing of an engine combustion chamber is used for this study. Effectiveness is measured by comparing the best feasible design obtained by each user, and efficiency is assessed based on the percentage of feasible designs generated by each user. Results indicate that novices who watch a 5-min training video before the experiment obtain results that are not significantly different from those obtained by expert users, and both groups are statistically better than the novices without the training video in terms of effectiveness and efficiency. Frequency and ordering of the visualization and exploration tools are also compared to understand the differences in each group’s search strategy. The implications of the results are discussed along with future work.

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.

Supporting Trade Space Exploration of Multi-Dimensional Data With Interactive Multi-Scale Nested Clustering and Aggregation

2009 ◽  
Author(s):  
Xiaolong Luke Zhang ◽  
Timothy W. Simpson ◽  
Mary Frecker ◽  
George Lesieutre
Keyword(s):  
Knowledge Discovery ◽  
Engineering Design ◽  
Space Exploration ◽  
Prototype System ◽  
Data Sets ◽  
Design Data ◽  
User Interactions ◽  
Multi Scale ◽  
Data Clusters ◽  
Trade Space Exploration

Knowledge discovery in multi-dimensional data is a challenging problem in engineering design. For example, in trade space exploration of large design data sets, designers need to select a subset of data of interest and examine data from different data dimensions and within data clusters at different granularities. This exploration is a process that demands both humans, who can heuristically decide what data to explore and how best to explore it, and computers, which can quickly identify features that may be of interest in the data. Thus, to support this process of knowledge discovery, we need tools that go beyond traditional computer-oriented optimization approaches to support advanced designer-centered trade space exploration and data interaction. This paper is an effort to address this need. In particular, we propose the Interactive Multi-Scale Nested Clustering and Aggregation (iMSNCA) framework to support trade space exploration of multi-dimensional data common to design optimization. A system prototype of this framework is implemented to allow users to visually examine large design data sets through interactive data clustering, aggregation, and visualization. The paper also presents a case study involving morphing wing design using this prototype system. By using visual tools during trade space exploration, this research suggests a new approach to support knowledge discovery in engineering design by assisting diverse user tasks, by externalizing important characteristics of data sets, and by facilitating complex user interactions with data.

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