Decision Making in Engineering Design

Professor Jitesh H. Panchal

Catalog Data

Role of decision making in engineering design; multi-objective decision making under risk and uncertainty; Group decision making; Sequential decision making; Model-based and data-driven decision making; Heuristics and biases in design decision making. Applications to engineering design including estimation of customer preferences, simulation-based design, and sustainable design. (3 credits)

Learning Outcomes

Upon completion of this course, students will be able to:

1. Formulate engineering design decisions under risk and uncertainty.
2. Apply multi-attribute utility theory to make decisions in engineering design.
3. Assess the assumptions and limitations of commonly used decision- making methods in engineering design.
4. Identify and reduce biases in engineering design decisions.
5. Adopt an interdisciplinary approach to design across engineering, economics, and social sciences.
    

Course Content

1. Course overview

    1.1    Module overview
    1.2    Why study decisions in engineering design (Part 1)?
    1.3    Why study decisions in engineering design (Part II)?
    1.4    Limitations of decision-making methods
    1.5    Decision-making in engineering design: The “How”
    1.6    Course structure

2. Engineering design and systems engineering through the lens of decision making

    2.1    Module overview
    2.2    Essence of systematic design methods
    2.3    Phase 1: Clarifying the task
    2.4    Quality Function Deployment (QFD)
    2.5    Phase 2: Conceptual design
    2.6    Pugh Selection
    2.7    Phase 3: Embodiment design
    2.8    Phase 4: Detail design phase
    2.9    Favorable properties of a selection method
    2.10    Systems Engineering
    2.11    The VEE model of Systems Engineering
    2.12    Decision analysis process
    2.13    Module summary

3. Framing a decision situation

    3.1    Module overview
    3.2    Alternative-focused vs. Value-focused thinking
    3.3    Decision basis and identification of decision situation
    3.4    Structuring objectives
    3.5    Structuring objectives (contd.)
    3.6    Attributes and alternatives
    3.7    Structuring decisions using Influence Diagrams and Decision Trees

4. Decisions under certainty

    4.1    Module overview
    4.2    The multi-attribute value problem
    4.3    The efficient frontier
    4.4    Structuring preferences: Lexicographic ordering
    4.5    Structuring preferences: Indifference curves and value functions
    4.6    Characteristics of preference structures: Marginal rate of substitution
    4.7    Characteristics of preference structures: Additive preferences
    4.8    Characteristics of preference structures: Conditional preferences

5. Probability Theory: An Overview

    5.1    Module overview
    5.2    Frequentist Theory of Probability
    5.3    Subjective Probability Theory
    5.4    Coherence
    5.5    Measurement of subjective probabilities
    5.6    Probability Calculus: Basic definitions
    5.7    Probability calculus: Bayes' rule and probability distributions

6. Single Attribute Utility Theory

    6.1    Module overview
    6.2    Alternate approaches to Risky Choice Problem
    6.3    Fundamentals of single-attribute utility theory
    6.4    Fundamentals of single attribute utility theory (contd.)
    6.5    Qualitative characteristics of utility
    6.6    Qualitative characteristics of utility (contd.)
    6.7    Attitudes to Risk
    6.8    Attitudes to Risk (contd.)
    6.9    Measuring risk aversion
    6.10    Measuring risk aversion (contd.)
    6.11    Procedures for assessing utility functions
    6.12    Module Summary

7. Multi-attribute Utility Theory

    7.1    Module overview
    7.2    Approaches for multi-attribute assessment
    7.3    Approaches for multi-attribute assessment (contd.)
    7.4    Conditional Utility independence
    7.5    Conditional Utility independence (contd.)
    7.6    Mutual Utility Independence
    7.7    Additive Independence and Additive Utility Function
    7.8    Utility Independence: More than Two Attributes
    7.9    Mutual Utility Independence and additive utility functions for More than two attributes
    7.10    Multiattribute assessment procedure
    7.11    Module summary

8. Multiattribute Utility Theory: Example Application in Design and Manufacturing

    8.1    Module overview: 3D printing application
    8.2    Overview of the example
    8.3    Assessing conditional utility functions
    8.4    Assessing scaling constants
    8.5    Choosing alternative based on Utility function

9. Value of Information

    9.1    Module overview and basic questions to be addressed
    9.2    Information acquisition: Illustrative examples and key concepts
    9.3    Expected value of perfect information
    9.4    Expected value of imperfect information
    9.5    Illustrative example

10. Sequential Decision Making

    10.1    Module overview
    10.2    Sequential Decision Making in engineering design
    10.3    Classification of sequential decision making problems
    10.4    Classification of sequential decision making problems (contd.)
    10.5    The dynamic programming algorithm - I
    10.6    The dynamic programming algorithm - II
    10.7    The dynamic programming algorithm - III
    10.8    Challenges in dynamic programming
    10.9    Approximate dynamic programming
    10.10    Approximate dynamic programming (contd.)

11. Rationality

    11.1    Module Overview and motivation
    11.2    Allais Paradox
    11.3    Tversky and Kahneman: Representativeness
    11.4    Tversky and Kahneman: Representativeness (contd.)
    11.5    Tversky and Kahneman: Availability
    11.6    Tversky and Kahneman: Adjustment and Anchoring
    11.7    Simon - Bounded Rationality
    11.8    Deviations from Expected Utility Theory
    11.9    Deviations from Expected Utility Theory (contd.)
    11.10    Generalizations of Expected Utility Theory
    11.11    Other deviations from Expected Utility Theory
    11.12    Cognitive Psychology perspective
    11.13    Descriptive, Normative and Prescriptive Models

12. Cumulative Prospect Theory

    12.1    Module overview and motivation for cumulative prospect theory
    12.2    Ingredients of cumulative prospect theory
    12.3    Probability weighting
    12.4    Rank dependent utility
    12.5    Reference dependence
    12.6    Combining the ingredients for Cumulative Prospect Theory

13. Decision Field Theory

    13.1    Module overview and motivation for Decision Field Theory
    13.2    Fundamental properties of human behavior
    13.3    Decision Field Theory: Stages 1 and 2
    13.4    Decision Field Theory: Stages 3 and 4
    13.5    Decision Field Theory: Stages 5 and 7
    13.6    Summary of decision field theory

14. Preferences over Time

    14.1    Module Overview
    14.2    The certainty case
    14.3    Constant discount rate
    14.4    Uncertain outcomes over time
    14.5    Uncertain time horizon

15. Estimating Customer Preferences

    15.1    Module overview
    15.2    Foundation - Random Utility Theory
    15.3    Special Case: The Logit model
    15.4    An Illustrative Example
    15.5    Power and Limitations of Logit
    15.6    Generalized Extreme Value (GEV)
    15.7    Probit and Mixed Logit models
    15.8    Summary of the module

16. Group Decision Making

    16.1    Module Overview and the social choice problem
    16.2    Aggregation rules
    16.3    Aggregation rules (contd.)
    16.4    Arrow's impossibility theorem
    16.5    Arrow's impossibility theorem - Proof outline
    16.6    Arrow's theorem in engineering design
    16.7    Aggregating individual's preferences - under certainty
    16.8    Aggregating individual's preferences - under uncertainty
    16.9    Considerations of equity
    16.10    Other issues and summary