Estimating Errors in Concept Selection

1995 ◽  
Author(s):  
K. N. Otto ◽  
Kristin L. Wood
Keyword(s):  
Error Propagation ◽  
Final Decision ◽  
Selection Methods ◽  
Concept Selection ◽  
Confidence Measure ◽  
Critical Design ◽  
Fundamental Causes ◽  
Design Alternatives ◽  
Degree Of Confidence ◽  
Numerical Concept

Abstract Numerical concept selection methods are used throughout industry to determine which among several design alternatives should be further developed. The results, however, are rarely believed at face value. Uncertainties (or errors) in subjective choices, modeling assumptions, and measurement are fundamental causes of this disbelief. This paper describes a methodology developed to predict overall error ranges, in addition to estimating a confidence measure in the numerical evaluation results. Each numerical assignment is given an associated error tolerance, and then treated as a probability error to create a simple means to propagate the uncertainties. A degree of confidence is also derived, similar to a statistical t-test, to indicate an induced confidence level in the final decision. Two preliminary concept selections are shown, to illustrate the methodology. Results from these concept selections indicate that (1) uncertainties can be suitably captured and quantified; (2) critical design questions are addressed during the process of numerical concept selection with error propagation; and (3) designers can make more informed and confident decisions through error estimation.

Utilization of Concept Selection Methods: A Survey of Finnish Industry

2005 ◽  
Author(s):  
Mikko Salonen ◽  
Matti Perttula
Keyword(s):  
Formal Methods ◽  
Design Research ◽  
Formal Method ◽  
Formal Concept ◽  
Concept Design ◽  
Selection Methods ◽  
Concept Selection ◽  
Analytic Hierarchy ◽  
Industrial Utilization ◽  
Concept Review

Concept selection is an area of design research that has been under considerable interest over the years. There is, however, only little information on how the methods that have been presented in design research for this task have been adopted by industry. Thus, a survey was carried out in the Finnish industry. The results revealed that the degree of industrial utilization of formal concept selection methods was relatively low. Less than one out of four companies responded to use one or several of the formal methods included in the study: Pugh’s evaluation matrix, Rating matrices, or Analytic Hierarchy Process (AHP). Concept review meeting were reported as the most common approach for concept selection. However, a majority of the companies that did not utilize any formal method reported lacking effective and suitable methods for concept selection. The companies using formal methods were more satisfied. The first conclusion from the study is that there is a basis for a higher degree of utilization of formal concept selection methods in industry. Our second conclusion is that the existing formal concept selection methods do not entirely fulfill the needs of concept selection in an industry context. We propose that numerical concept selection methods should be further developed and extended to better support the decision-making practices of concept selection in industry. This type of concept selection is characterized by the participation of multiple decision makers through concept design reviews.

A Multi-Level Approach to Concept Selection in Sustainable Design

2014 ◽  
Author(s):  
Daniel Stratton ◽  
Sara Behdad ◽  
Kemper Lewis ◽  
Sundar Krishnamurty
Keyword(s):  
Environmental Sustainability ◽  
Utility Score ◽  
Concept Selection ◽  
Trade Offs ◽  
Design Alternatives ◽  
Multi Level ◽  
Early Phases ◽  
Different Levels ◽  
Selection Of ◽  
Lca Results

The motivation behind this work is to integrate economic and environmental sustainability into decision making at the early phases of design through the development of a hierarchical concept selection method. Life Cycle Assessment (LCA) is a frequently implemented technique used to assess the environmental impacts of products, but it does not provide a simple means for including preference at different levels that can be used for comparison across design alternatives. A method is proposed to accommodate this issue expanding the Hypothetical Equivalents and Inequivalents Method (HEIM) to handle multi-level and multi-attribute trade-offs. The selection of a coffee maker design is used as an example to illustrate the implementation of the method with actual LCA results. The example provides valuable insights into how preferences may be elicited at different hierarchical levels and then combined to create a single utility score that represents to what extent each design alternative is preferred by the decision maker.

A Non-Deterministic Approach to Concept Selection Using S-Pareto Frontiers

2002 ◽  
Author(s):  
Christopher A. Mattson ◽  
Achille Messac
Keyword(s):  
Design Parameters ◽  
Concept Selection ◽  
Traditional Use ◽  
Design Decisions ◽  
Traditional Concept ◽  
Critical Design ◽  
Model Fidelity ◽  
Engineering Design Process ◽  
Definition Of ◽  
Pareto Frontiers

The most significant design decisions are typically made during the conceptual phase of the engineering design process, when critical design features are proposed, evaluated and selected. In this paper, we explore the critical task of concept selection and propose a non-deterministic, optimization-based approach for selecting the most promising concept. The method presented in this paper builds upon the recently-proposed s-Pareto based concept selection approach. Within the framework of the s-Pareto approach, so-called s-Pareto frontiers are obtained by using the definition of Pareto optimality to identify Pareto optimal solutions that pertain to a set of distinct concepts. These s-Pareto frontiers are used to assess the tradeoffs between various proposed concepts during conceptual design. The s-Pareto approach is a marked departure from traditional concept selection methods and from the traditional use of Pareto frontiers. In this work the s-Pareto approach is extended to include uncertainties caused by stochastic design parameters as well as low model fidelity. More specifically, the reliability of design decisions is accounted for in the decision-making process. Two approaches are presented for performing non-deterministic concept selection. Two examples are given that support the approach.

A causality-inspired feature selection method for cancer imbalanced high-dimensional data

2021 ◽  
Author(s):  
Yijun Liu ◽  
Qiang Huang ◽  
Huiyan Sun ◽  
Yi Chang
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Class Imbalance ◽  
Selection Method ◽  
Learning Performance ◽  
High Dimensional ◽  
Final Decision ◽  
Selection Methods ◽  
Cancer Initiation ◽  
Cancer Types

It is significant but challenging to explore a subset of robust biomarkers to distinguish cancer from normal samples on high-dimensional imbalanced cancer biological omics data. Although many feature selection methods addressing high dimensionality and class imbalance have been proposed, they rarely pay attention to the fact that most classes will dominate the final decision-making when the dataset is imbalanced, leading to instability when it expands downstream tasks. Because of causality invariance, causal relationship inference is considered an effective way to improve machine learning performance and stability. This paper proposes a Causality-inspired Least Angle Nonlinear Distributed (CLAND) feature selection method, consisting of two branches with a class-wised branch and a sample-wised branch representing two deconfounder strategies, respectively. We compared the performance of CLAND with other advanced feature selection methods in transcriptional data of six cancer types with different imbalance ratios. The genes selected by CLAND have superior accuracy, stability, and generalization in the downstream classification tasks, indicating potential causality for identifying cancer samples. Furthermore, these genes have also been demonstrated to play an essential role in cancer initiation and progression through reviewing the literature.

A study of the use of concept selection methods from inside a company

2010 ◽  
Vol 22 (1) ◽  
pp. 7-27 ◽  
Author(s):  
Belinda López-Mesa ◽  
Nicklas Bylund
Keyword(s):  
Selection Methods ◽  
Concept Selection ◽  
A Company

scholarly journals An ad hoc decision support method over additive vs. conventional manufacturing

2020 ◽  
Vol 318 ◽  
pp. 01028
Author(s):  
Stamatios Polydoras ◽  
Clio Vossou ◽  
Dimitrios Koulocheris
Keyword(s):  
Ad Hoc ◽  
Time Pressure ◽  
Mechanical Design ◽  
Final Decision ◽  
Critical Design ◽  
Design Flexibility ◽  
Short Run ◽  
Successful Design ◽  
Recent Trends ◽  
Design Factors

The mechanical design process considers numerous factors. Requirements related to performance and quality, limitations by legislation, standards, methods utilized or technological boundaries, urgency, cost, data preparation and preservation, design flexibility and organizational aspects. Successful design consists of proper decisions on form, geometry, materials, manufacturing methods, quality, reliability and more. Nowadays, a critical decision during design and realization of technological objects is whether they should be made conventionally or with Additive Manufacturing (AM)/3D Printing methods. Such a decision occurs under time-pressure or via a broader strategy for technological switch, is complex, multi-parametric and bears uncertainty and risk. A simple, effective and substantiated method to assist decisions for switching from conventional to AM could prove very useful. This paper refers to recent trends and activity in international AM-related standards, then presents and discusses preliminary work of the authors for an ad hoc decision method to be used upon specific “go/ no-go” decisions for AM. The method is largely based on the Pareto principle, to limit critical design factors contributing to this decision. All steps of the method towards a final decision are described. The method is demonstrated with a hypothetical, yet realistic example of a short run coolant vessel manufacture.

On Rank Reversals in the Borda Count

2003 ◽  
Author(s):  
Michael J. Scott ◽  
Irena Zivkovic
Keyword(s):  
Engineering Design ◽  
Pairwise Comparison ◽  
Comparison Method ◽  
Borda Count ◽  
Concept Selection ◽  
Design Alternatives

The Borda count is a pairwise comparison method which can be shown to have various desirable properties. Questions have been raised about the efficacy of the Borda count and its engineering equivalents for concept selection in engineering design. Of chief concern is the possibility of rank reversals among design alternatives when other alternatives are added or dropped from consideration. Results from simulations are presented that show the likelihood that Borda count comparisons will result in rank reversals upon modifications of the set of alternatives. The available evidence indicates that rank reversals in the Borda count are generally restricted to alternatives that are difficult to distinguish.

How Do I Choose? The Influence of Concept Selection Methods on Student Team Decision-Making

2016 ◽  
Author(s):  
Xuan Zheng ◽  
Scarlett R. Miller
Keyword(s):  
Design Education ◽  
Assessment Method ◽  
First Year ◽  
Thought Processes ◽  
Selection Methods ◽  
Concept Selection ◽  
Final Design ◽  
Team Decision ◽  
Design Ideas ◽  
Student Team

While research has been conducted to study the use of concept selection methods in design education, few studies have focused on the influence of these methods on individual students’ and teams’ thought processes in grade-dependent class projects. In order to fill this research gap, the current study was designed to compare the influence of two concept selection methods, the Concept Selection Matrix (CSM) and a new adjective assessment method called the Tool for Assessing Semantic Creativity (TASC), through an experimental study in two sections of a first year engineering design class. The results of the study show that while students were equally confident in the concept ratings from the CSM and TASC methods, they reported that they were more likely to select ideas ranked highly in the CSM method. However, subsequent analysis revealed no difference between the common elements in the ideas rated highly by the two methods and the final design ideas produced.

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