Linking Properties of Design Problems to Optimal Team Characteristics

2016 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Structural Design ◽  
Design Problem ◽  
Design Teams ◽  
Objective Functions ◽  
Team Size ◽  
Team Behavior ◽  
Design Problems ◽  
Significant Relationships ◽  
Team Characteristics ◽  
The Right

A team with the right characteristics can exceed the sum of their individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is crucial that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of a design problem can be used to select the best values for team characteristics. Two characteristics are considered: team size and interaction frequency. A computational model of design teams that has been shown to effectively emulate human team behavior is leveraged to pinpoint optimized team characteristics for solving a variety of fluid and structural design problems. The nature of each design problem is characterized with respect to local and global behavior of the design space, alignment between objective functions, and the resources allotted for solving the problem. Regression analysis is used to create equations for predicting optimized team characteristics based on problem properties. These equations, which enable the informed design of design teams based on those characteristics, describe statistically significant relationships and are found to have useful levels of accuracy. Further analysis reveals insights about how the properties of a design problem can influence a team’s search for solutions.

scholarly journals Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Empirical Test ◽  
Configuration Design ◽  
Design Teams ◽  
Design Problems ◽  
Team Members ◽  
Optimizing Design ◽  
Team Characteristics ◽  
Cognitive Study ◽  
The Right ◽  
Predictive Theory

The performance of a team with the right characteristics can exceed the mere sum of the constituent members’ individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team’s search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

scholarly journals Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Empirical Test ◽  
Configuration Design ◽  
Design Teams ◽  
Design Problems ◽  
Team Members ◽  
Optimizing Design ◽  
Team Characteristics ◽  
Cognitive Study ◽  
The Right ◽  
Predictive Theory

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

scholarly journals DESIGN PROBLEM PERCEPTION IN ENGINEERING DESIGN TEAMS

2018 ◽  
Author(s):  
Amirali Ommi ◽  
Yong Zeng ◽  
Catharine C. Marsden
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Design Teams ◽  
Test Bed ◽  
Descriptive Model ◽  
Design Problems ◽  
Team Mental Models ◽  
Problem Perception ◽  
Design Creativity ◽  
Proposed Model

 Abstract – Engineering design is a decision making process that needs a good perception of the design problem to be solved. Design problems are usually solved in a team. Teams need the existence of a good design problem perception to create design solutions. This study provides an approach for elaborating a descriptive model to describe how the perception process works within a design team. This study is going to propose an approach for integrating a theoretical model of design creativity with team mental models, so they can be used for elaborating the descriptive model of perception in design teams. The NSERC Chair in Aerospace Design Engineering (NCADE) at Concordia University holds a capstone project which will be considered to be used as a test bed for validating proposed model through experimental analysis. Proposed experiments and further research are introduced at the end of paper.

scholarly journals Designing the Characteristics of Design Teams via Cognitively Inspired Computational Modeling

2018 ◽  
Author(s):  
Christopher McComb
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Optimization Algorithm ◽  
Engineering Students ◽  
Design Teams ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Holistic Understanding ◽  
Team Characteristics

Teams are a ubiquitous part of the design process and a great deal of time and effort is devoted to managing them effectively. Although teams have the potential to search effectively for solutions, they are also prone to a number of pitfalls. Thus, a greater understanding of teams is necessary to ensure that they can function optimally across a variety of tasks. Teams are typically studied through controlled laboratory experiments or through longitudinal studies that observe teams in situ. However, both of these study types can be costly and time-consuming. Months, if not years, pass between the initial conception of a study and the final analysis of results. This work creates a computational framework that efficiently emulates human design teams, thus facilitating the derivation of a theory linking the properties of design problems to optimized team characteristics, effectively making it possible to design design teams.This dissertation first introduces and validates the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework. The central structure of CISAT is modeled after simulated annealing, a global optimization algorithm that has been shown to effectively mimic the problem-solving process of individuals. Specifically, a multi-agent analog of simulated annealing is used in CISAT to mimic the behavior of teams. Several additional components, drawn from the psychology and problem-solving literature, are then included in the framework to enable a more accurate description of individual activity and interaction within the team. CISAT is then used to investigate the relationship between design problem properties, team characteristics, and task performance. Multiple computational simulations are conducted in which simulated teams with various characteristics solve a variety of different configuration problems. These simulations are then post-processed to produce a set of equations that make it possible to predict optimal team characteristics based on problem properties, thus enabling the optimal design of design teams. To validate these equations a behavioral study is designed and conducted in which teams of engineering students interact at different frequencies while designing a complex system. Results of the study offer a limited validation of the predictive equations.This dissertation further highlights the resource efficiency and versatility of CISAT by demonstrating its use in two additional applications. In the first, a new numerical optimization algorithm is derived directly from CISAT by stripping away all but the most quintessential team-based characteristics. The team-based characteristics of this algorithm allow it to achieve high performance across a variety of objective function with diverse topographies. In the second application, CISAT is used in conjunction with Markov concepts to examine the order in which designers make changes to their solutions. Although it has been demonstrated that humans apply changes in a specific order (called a sequence) when solving puzzles, such patterns have not been examined for engineers solving design problems. It is shown that operation sequences are used by designers, and improve solution quality. This dissertation demonstrates how characteristics of individual designers and design teams can be captured and accurately reproduced within a computational model to advance our knowledge of design methodology. Future extensions of this work have the potential to inform a deeper and more holistic understanding of the search process.

scholarly journals The relationship between personal intrinsic factors towards a design problem and the degree of novelty and circularity

2021 ◽  
Author(s):  
Laura Ruiz-Pastor ◽  
Vicente Chulvi ◽  
Elena Mulet ◽  
Marta Royo
Keyword(s):  
Design Problem ◽  
Design Teams ◽  
Design Problems ◽  
Intrinsic Factors ◽  
State Action ◽  
Level Of Knowledge ◽  
The Difference ◽  
Causality Model ◽  
The Relationship ◽  
Selection Of

AbstractThe aim of this work is to determine how personal intrinsic factors towards a design problem are related to novelty and circularity. A deeper understanding of this relationship will be a valuable aid when it comes to making an adequate selection of design teams. The factors studied are the level of the designer's motivation, relevance, knowledge and affinity with regard to the design problem. To this end, a study was conducted with 35 novice designers, organised in groups of between two and five members. Each group had to propose a conceptual solution to two different design problems. Novelty was assessed using the SAPPhIRE causality model (which stands for State–Action–Part–Phenomenon–Input–oRgan–Effect) and the Circular Economy Toolkit was applied to measure circularity. The results show that as motivation, level of knowledge, perception of relevance and affinity for the problem increase, the solution displays greater novelty and less circularity, although for circularity, the difference is not statistically significant.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

scholarly journals Do Empathic Individuals Behave More Prosocially? Neural Correlates for Altruistic Behavior in the Dictator Game and the Dark Side of Empathy

2021 ◽  
Vol 11 (7) ◽  
pp. 863
Author(s):  
Michael Schaefer ◽  
Anja Kühnel ◽  
Franziska Rumpel ◽  
Matti Gärtner
Keyword(s):  
Dictator Game ◽  
Empathic Concern ◽  
Dark Side ◽  
Altruistic Behavior ◽  
Temporoparietal Junction ◽  
Specific Effects ◽  
Significant Relationships ◽  
Right Temporoparietal Junction ◽  
Neural Underpinnings ◽  
The Right

Do empathic individuals behave more prosocially? When we think of highly empathic individuals, we tend to assume that it is likely that those people will also help others. Most theories on empathy reflect this common understanding and claim that the personality trait empathy includes the willingness to help others, but it remains a matter of debate whether empathic individuals really help more. In economics, a prominent demonstration that our behavior is not always based on pure self-interest is the Dictator Game, which measures prosocial decisions in an allocation task. This economic game shows that we are willing to give money to strangers we do not know anything about. The present study aimed to test the relationship between dispositional empathy and prosocial acting by examining the neural underpinnings of prosocial behavior in the Dictator Game. Forty-one participants played different rounds of the Dictator Game while being scanned with functional magnetic resonance imaging (fMRI). Brain activation in the right temporoparietal junction area was associated with prosocial acting (number of prosocial decisions) and associated with empathic concern. Behavioral results demonstrated that empathic concern and personal distress predicted the number of prosocial decisions, but in a negative way. Correlations with the amount of money spent did not show any significant relationships. We discuss the results in terms of group-specific effects of affective empathy. Our results shed further light on the complex behavioral and neural mechanisms driving altruistic choices.

Evaluating and Improving Locomotive Crashworthiness Using LS-DYNA

2000 ◽  
Author(s):  
Stephen J. Kokkins ◽  
S. Kash Kasturi ◽  
Wayne Kong ◽  
S. K. John Punwani
Keyword(s):  
Finite Element ◽  
Structural Design ◽  
Structural Models ◽  
Structural Behavior ◽  
Right Of Way ◽  
Dynamic Finite Element ◽  
Rail Vehicles ◽  
Linear Material ◽  
The Right ◽  
Modeling Code

Abstract Representative structural models of locomotives, other rail vehicles, and other potential colliding objects were combined into moving consists which were then subjected to various collision scenarios. The LS-DYNA dynamic finite-element modeling code was utilized to realistically simulate collisions and guide understanding and improvements of the locomotive structures. This incorporated the effects of the collision interactions, plus critical non-linear material and structural behavior, buckling, fracture, kinematics, and wayside interactions of the vehicles. The types of collisions included: locomotive-headed consists striking standing consists obliquely fouling the Right Of Way, headed both by freight cars and other locomotives; locomotives striking loose, loaded intermodal containers dislodged from opposing cars on adjacent track. Work on multiple coupled locomotive overrides in direct consist collisions is now being conducted. The effects of varying parameters such as collision speeds, location and orientation of the colliding vehicles, and structural improvements were explored and quantified. Also, the effects of some structural design modifications such as stronger collision posts and cab structure were evaluated using this process. Verification studies to date have shown good correlation between the analytical simulations and observed outcome of actual historical accidents.

Design Option Decision Tree: A Method for Schematic Analysis of a Design Problem and Integration of Human Factors Data

1974 ◽  
Vol 18 (3) ◽  
pp. 368-375
Author(s):  
William B. Askren ◽  
Kenneth D. Korkan
Keyword(s):  
Decision Tree ◽  
Human Factors ◽  
Design Problem ◽  
Aircraft Design ◽  
Decision Point ◽  
Specific Design ◽  
Design Problems ◽  
Design Option ◽  
Design Options ◽  
Evaluation Parameters

A Design Option Decision Tree (DODT) is a graphic means of showing the design options available at each decision point in the design process. Several examples of DODTs for aircraft design problems are shown. The procedures for developing a DODT are described. A proposed method for use of the DODT to resolve a design problem is presented. This method includes evaluating the design options in the Tree for impact on the system, and tracing paths through the Tree as dictated by specific design goals. The use of human factors data as one of the evaluation parameters is illustrated. The paper concludes with a discussion of other uses of a DODT.

Sign in / Sign up

Export Citation Format

Share Document