Decision Topology Assessment in Engineering Design Under Uncertainty

2014 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Matthew P. Castanier
Keyword(s):  
Decision Analysis ◽  
Engineering Design ◽  
Decision Maker ◽  
Mathematical Optimization ◽  
Linear Functions ◽  
Design Decision ◽  
Utility Analysis ◽  
Decision Making Problem ◽  
Single Attribute ◽  
Incremental Construction

The implications of decision analysis (DA) on engineering design are well known. Recently, the authors proposed decision topologies (DT) as a visual method for making design decisions and proved that they are consistent with normative decision analysis. This paper addresses the practical issue of assessing DTs for a decision maker (DM) using their responses, particularly under uncertainty. This is critical to encoding decision maker preferences so that further analysis and mathematical optimization can be performed using the correct set of preferences. We show how multiattribute DTs can be directly assessed from DM responses. Four methods are shown to evolutionarily assess DTs among which one that requires the DM to rank alternatives and another where a utility function is first assessed. It is also shown that preferences under uncertainty can be easily incorporated. In addition, we show that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction. The reverse problem of inferring single attribute DTs is also presented. The proposed assessment methods are used on a design decision making problem of a welded beam.

Flaws Lurking in Engineering Design-Decision Making: The Attribute Set Dissociation Problem

2016 ◽  
Author(s):  
Vijitashwa Pandey
Keyword(s):  
Decision Analysis ◽  
Engineering Design ◽  
Fundamental Problem ◽  
Design Decision ◽  
Design Problems ◽  
Design Decisions ◽  
Root Cause ◽  
Multiple Attributes ◽  
Lower Limits ◽  
Specific Definition

The applicability of theoretical decision analysis, while rationally sound, has eluded mainstream engineering design. A reason commonly overlooked is that basic concepts in decision analysis do not scale naturally to multiple attributes — which are encountered in, by far, most design problems. In this paper, we document a paradox when dealing with transactions involving multiple attributes. We show the possibility of a money pump where if we dissociate part of an attribute from a design, the rest of the design can be manipulated to get either a better design or create wealth out of nothing. To reconcile with paradox, it is argued that there is a fundamental problem dealing with multiple attributes where a frame of reference chosen (purposefully) ignores external inputs, assuming that design decisions happen in the vacuum of the frame chosen. For example, in a simple design valuation decision, the money amount committed does not necessarily come from a fixed range of negotiability (upper and lower limits) but is subject to change if significant changes in other attributes are possible. The root cause of this issue is that fungible attributes such as money can form a part of the attribute set or be trivially dissociated from it, if needed. We argue that this is rational behavior on a decision maker’s part. However, most utility formulations do not model it and lead to the paradox. We call this the attribute dissociation problem. A specific definition is provided as well as implications on design as well as preference elicitation methods are considered. Finally, formulations are presented that avoid this problem and recommendations are provided.

Limitations of Pareto Front in Design Under Uncertainty and Their Reconciliation

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Efstratios Nikolaidis
Keyword(s):  
Utility Function ◽  
Engineering Design ◽  
Decision Maker ◽  
Decision Problem ◽  
Pareto Front ◽  
Optimization Problem ◽  
Reduction Gear ◽  
Design Under Uncertainty ◽  
Design Constraints ◽  
Single Attribute

Engineering design reconciles design constraints with decision maker (DM) preferences. The task of eliciting and encoding decision maker preferences is, however, extremely difficult. A Pareto front representing the locus of the nondominated designs is, therefore, often generated to help a decision maker select the best design. In this paper, we show that this method has a shortcoming when there is uncertainty in both the decision problem variables and in the model of decision maker's preferences. In this case, the Pareto front is inconsistent with multi-attribute utility (MAU) theory, unless the decision maker trades off attributes or some functions of them linearly. This is a strong restriction. To account for this, we propose a methodology that enables a decision maker to select the best design on a modified pareto front (MPF) which is acquired using envelopes of a set of certainty equivalent (CE) surfaces. The proposed method does not require separability of the multi-attribute utility function into single-attribute utilities, nor does it require the decision maker to trade the attributes (or any function of them) linearly. We demonstrate our approach on a simple optimization problem and in design of a reduction gear.

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

Subjective Design Evaluation With Multiple Attributes

1990 ◽  
Author(s):  
Deborah L. Thurston
Keyword(s):  
Automotive Industry ◽  
The United States ◽  
Quantitative Information ◽  
Design Decision ◽  
Utility Analysis ◽  
Materials Selection ◽  
Design Alternatives ◽  
Multiattribute Utility Analysis ◽  
Alternative Designs

Abstract A formal methodology is presented which may be used to evaluate design alternatives in the iterative design/redesign process. Deterministic multiattribute utility analysis is used to compare the overall utility or value of alternative designs as a function of the levels of several performance characteristics of a manufactured system. The evaluation function reflects the designers subjective preferences. Sensitivity analysis provides quantitative information as to how a design should be modified in order to increase its utility to the design decision maker. Improvements in one or more areas or performance and tradeoffs between attributes which would increase desirability of a design most may be quantified. A case study of materials selection and design in the automotive industry is presented. The methodology was applied to 6 automotive companies in the United States and Europe, and results are used to illustrate the steps followed in application.

scholarly journals Selection and Prioritization of Software Requirements Applying Verbal Decision Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20
Author(s):  
Paulo A. M. Barbosa ◽  
Plácido R. Pinheiro ◽  
Francisca R. V. Silveira ◽  
Marum Simão Filho
Keyword(s):  
Software Development ◽  
Decision Analysis ◽  
Decision Maker ◽  
Development Process ◽  
Software Requirements ◽  
Software Development Process ◽  
Next Release Problem ◽  
Support Tools ◽  
Verbal Decision Analysis ◽  
The Cost

During the software development process, the decision maker (DM) must master many variables inherent in this process. Software releases represent the order in which a set of requirements is implemented and delivered to the customer. Structuring and enumerating a set of releases with prioritized requirements represents a challenging task because the requirements contain their characteristics, such as technical precedence, the cost required for implementation, the importance that one or more customers add to the requirement, among other factors. To facilitate this work of selection and prioritization of releases, the decision maker may adopt some support tools. One field of study already known to solve this type of problem is the Search-Based Software Engineering (SBSE) that uses metaheuristics as a means to find reasonable solutions taking into account a set of well-defined objectives and constraints. In this paper, we seek to increase the possibilities of solving the Next Release Problem using the methods available in Verbal Decision Analysis (VDA). We generate a problem and submit it so that the VDA and SBSE methods try to resolve it. To validate this research, we compared the results obtained through VDA and compared with the SBSE results. We present and discuss the results in the respective sections.

scholarly journals Reasoning with linguistic preferences using NPN logic

2010 ◽  
Vol 7 (3) ◽  
pp. 511-528 ◽  
Author(s):  
Goran Devedzic ◽  
Danijela Milosevic ◽  
Lozica Ivanovic ◽  
Dragan Adamovic ◽  
Miodrag Manic
Keyword(s):  
Side Effects ◽  
Decision Analysis ◽  
Decision Maker ◽  
Real World ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Basic Notion ◽  
The Real ◽  
Alternative Framework ◽  
Linguistic Models

Negative-positive-neutral logic provides an alternative framework for fuzzy cognitive maps development and decision analysis. This paper reviews basic notion of NPN logic and NPN relations and proposes adaptive approach to causality weights assessment. It employs linguistic models of causality weights activated by measurement-based fuzzy cognitive maps? concepts values. These models allow for quasi-dynamical adaptation to the change of concepts values, providing deeper understanding of possible side effects. Since in the real-world environments almost every decision has its consequences, presenting very valuable portion of information upon which we also make our decisions, the knowledge about the side effects enables more reliable decision analysis and directs actions of decision maker.

scholarly journals VERBAL DECISION MAKING ANALYSIS

2004 ◽  
Vol 10 (1) ◽  
pp. 32-39
Author(s):  
Dmitry Kochin ◽  
Leonas Ustinovičius
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Decision Maker ◽  
Mathematical Proof ◽  
Preference Elicitation ◽  
Qualitative Approach ◽  
Analysis Methods ◽  
Verbal Decision Analysis ◽  
Quantitative Approaches

The paper presents the ideology of a qualitative approach to decision making ‐ verbal decision analysis. The authors have analyzed existing quantitative approaches and pointed out their main disadvantages. They formulated the requirements for decision‐making methods taking into account these disadvantages: psychological correctness of a dialog with decision maker (DM), strict mathematical proof of the methods and checking of DM information for consistency. The authors present the results of research on psychological correctness of operations of preference elicitation from DM. Several existing verbal decision analysis methods are briefly mentioned.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

A Multidisciplinary Framework to Model Complex Team-Based Product Development

2015 ◽  
Author(s):  
Shun Takai
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Product Development ◽  
Decision Analysis ◽  
Equilibrium Analysis ◽  
Design Decision ◽  
Behavioral Game Theory ◽  
Model Complex ◽  
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 in a team project and individual projects. The proposed framework integrates cooperative and noncooperative design models with (1) equilibrium analysis, (2) uncertainty modeling based on behavioral game-theory results, and (3) decision-making using decision analysis. In the proposed framework, noncooperative design is used to simulate engineers’ decisions about team project commitment and to analyze potential free-riding; cooperative design is used to model design outcomes when engineers collaborate in the team project; equilibrium analysis and behavioral game-theory results are used to infer about other engineers’ decisions; and 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.

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