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.

Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

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.

scholarly journals TOPSIS-RTCID for range target-based criteria and interval data

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
A. Jahan ◽  
M. Yazdani ◽  
K.L. Edwards
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Interval Data ◽  
Interesting Property ◽  
Design Decision ◽  
Reversal Problem ◽  
Structured Decision Making ◽  
Analysis Technique ◽  
Engineering Design Problems ◽  
Order Preference

<p>The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.</p>

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.

A Multidisciplinary Framework to Model Complex Team-Based Product Development

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Shun Takai
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Product Development ◽  
Decision Analysis ◽  
Collaborative Design ◽  
Equilibrium Analysis ◽  
Design Decision ◽  
Behavioral Game Theory ◽  
Behavioral Game ◽  
Team Project

This paper investigates a multidisciplinary framework that simulates design decisions in a complex team-based product development in which engineers simultaneously work on a team project and individual projects. The proposed framework integrates collaborative design with (1) equilibrium analysis, (2) uncertainty modeling based on behavioral game-theory results, and (3) noncooperative decision making using decision analysis. In the proposed framework, noncooperative decision making is used to simulate engineers’ decisions about team-project commitment and to analyze potential free riding. Collaborative design is used to model design outcomes when engineers commit to the team project. Equilibrium analysis and behavioral game-theory results are used to infer uncertainty about other engineers’ decisions. Decision analysis is used to calculate expected values of decision alternatives. The proposed framework and the design decision making model are illustrated using a pressure vessel design as a team project conducted by two engineers: a design engineer and a materials engineer.

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.

Incorporating Social Product Development in Distributed Collaborative Design Education

2013 ◽  
Author(s):  
Dazhong Wu ◽  
Merlin Morlock ◽  
Prateek Pande ◽  
David W. Rosen ◽  
Dirk Schaefer
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
Social Networking Sites ◽  
Collaborative Design ◽  
Design Education ◽  
Design Problems ◽  
Mass Collaboration ◽  
Engineering Design Problems ◽  
Social Product ◽  
Product Realization

Over the past few years, Social Product Development (SPD) has emerged as a new trend to improve traditional engineering design and product realization processes. SPD involves the concepts of crowdsourcing, mass collaboration, customer co-creation, and most recently cloud-based design and manufacturing. One of the key characteristics of SPD is to apply social computing techniques (e.g., social networking sites and online communities) to support different phases of product realization processes. In line with this trend, our objective is to help our students become familiar with this paradigm shift and learn how to solve engineering design problems in a distributed and collaborative setting. Consequently, we have experimented with introducing some aspects of SPD into one of our graduate level engineering design courses. In this paper, we (1) introduce a SPD process that is implemented in the course, (2) present a case study from one of the design teams, and (3) share our experience and lessons with respect to the implementation of the SPD process.

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