Problem solving for engineers

Machine generated contents note: 1. Systems Methodology

1.1. Introduction

1.2. Terminology

1.2.1. Abuse of Terminology

1.2.2. Probabilistic, Deterministic

1.2.3. Dynamic, Static

1.2.4. Discrete, Continuous

1.3. Origin

1.4. System

1.4.1. Relation between Parts

1.5. Fundamental Variables

1.5.1. Output, State

1.5.2. Input, Control

1.5.3. Disturbance

1.6. Subsystems

1.7. Environment

1.8. System Boundary

1.8.1. Open and Closed Systems

1.9. System and Behavior Characterization

1.9.1. Transient and Steady States

1.9.2. Equilibrium

1.9.3. Equifinality

1.9.4. Stability

2. Models and Modeling

2.1. Introduction

2.1.1. Terminology

2.2. Formalism

2.3. Hierarchical Multilevel Systems

2.3.1. States, Controls, and Outputs

2.3.2. Single Level

2.4. Staged Systems

2.4.1. Project Phases

2.5. Model Development

2.5.1. Scientific Method

2.5.1.1. Nonrigorous and Management Models

2.5.2. False Causality

2.5.3. Adages, Truisms.

Contents note continued: 2.6. Classification

2.7. System Model Terminology

2.7.1. Terminology

2.7.2. Automatic Control

2.7.3. Fundamental Configurations

3. Some Common System Models

3.1. Introduction

3.2. Block Diagrams

3.2.1. Junctions

3.2.2. Series Subsystems

3.2.3. Parallel Subsystems

3.2.4. Feedback

3.3. Black Box

3.4. State Equation Models

3.4.1. Differential Equation Models

3.4.2. Difference Equation Models

3.4.3. Partial Differential Equation Models

3.4.4. Algebraic (Nondifference) Equation Models

3.5. Other Forms

3.5.1.Networks

3.5.2. Queuing Models

3.5.3. Trees

3.5.4. Simulation

4. Fundamental Configurations Relating to Systems

4.1. Introduction

4.2. Analysis

4.2.1. Simulation

4.2.2. Prediction, Forecasting

4.2.3. Stability

4.2.4. Sensitivity

4.2.5. Economic Appraisal

4.2.6. Critical Path Method (CPM)

4.2.7. Reliability

4.2.8. Queuing Theory

4.2.9. Fault Trees, Event Trees.

Contents note continued: 4.2.10. Other Forms of Analysis

4.2.11. Garbage In-Garbage Out

4.2.12. Modeling and Analysis

4.3. Synthesis

4.3.1. Confusion in Terminology Usage

4.3.2. Conversion to an Iterative Analysis Form

4.3.3. On-Line/Off-Line

4.4. Investigation

4.4.1. State Estimation

4.4.2. Natural/Artificial Input

4.4.3. Roots

4.4.4. On-Line/Off-Line

4.5. Controllability and Observability

4.5.1. Controllability

4.5.2. Observability

5. The Synthesis Configuration

5.1. Introduction

5.1.1. Objective

5.1.2. Constraints

5.1.3. Variables

5.1.4. Decision Support

5.2. Conversion to Iterative Analysis

5.2.1. Steps

5.2.2. Planning

5.2.3. Systems Engineering

5.2.4. Direct Synthesis

5.2.5. Theory of Optimal Control Systems

5.3. Optimal Form of Synthesis

5.3.1.(Optimal) Synthesis Components

5.3.2. Constraints

5.3.3. Objective

5.3.4. Probabilistic Systems

5.3.5. Multiple Objectives

5.3.6. Alternative Terminology.

Contents note continued: 5.4. Design Examples

5.4.1. Structural Design

5.4.2. Earthmoving Operation

5.5. Optimization Techniques

5.5.1."Gut Feel" Considerations

5.5.2. Numerical Approaches

5.5.3. Calculus

5.5.4. Pontryagin's Maximum Principle

5.5.5. Dynamic Programming

5.5.6. Mathematical Programming

5.6. Project Planning

5.6.1. As Synthesis

5.6.2. As Iterative Analysis

5.6.3. Project and End-Product

5.6.4. Conventional Thinking on Planning

5.6.5. Planning Function

5.6.6. Planning Components

5.6.7. Project "Control"

5.6.8. Financial Planning

5.6.9. Human Resource Planning

5.6.10. Resource Planning

5.6.11. Strategic Planning

5.7. Management

5.7.1. General Management

5.7.2. Project Management

5.7.2.1."Time" Management

5.8. Risk Management

5.8.1. Risk Management Process

5.8.2. Definition

5.8.3. Objectives and Constraints Statement

5.8.4. Alternatives Generation

5.8.5. Analysis

5.8.6. Evaluation.

Contents note continued: 5.8.7. Iteration Feedback

5.9. Work Study

5.9.1. Outline

5.9.2. Method Study

5.9.3. Work Measurement

5.9.4. Reengineering

5.10. Value Management

5.10.1. Process

5.10.2. Distinguishing Features

5.11. Constructability

6. The Investigation Configuration

6.1. Introduction

6.1.1. State Estimation

6.1.2. Natural/Artificial Input

6.1.3. Roots

6.1.4. On-Line/Off-Line

6.2. Black and Gray Boxes

6.2.1. Parameter Estimation

6.3. System Response/Output

6.3.1. Linearity

6.3.2. Linear Differential Equation Models

6.4. Least Squares Approach

6.4.1. Static System Models

6.4.2. Data Grouped

6.4.2.1. Generalization

6.4.3. Weighted Least Squares

6.4.4. Sequential Least Squares

6.4.4.1. Multiple Parameter Case

6.4.5. Nonlinear Regression

6.4.6. Multiple Linear Regression

6.4.7. Correlation

6.5. Forecast Modeling

6.5.1. Introduction

6.5.1.1. Modeling without Historical Data

6.5.2. Patterns

6.5.2.1. Noise.

Contents note continued: 6.5.2.2. Stationarity

6.5.2.3. Dependence

6.5.3. Forecast Error

6.5.3.1. Definitions

6.5.3.2. Mean Absolute Deviation

6.5.3.3. Bias

6.5.3.4. Mean Absolute Percentage Error

6.5.3.5. Mean Squared Error

6.5.3.6.Comment

6.5.4. Models

6.5.4.1. Choice of Model

6.5.5. Expert Judgment

6.5.6. Delphi Approach

6.5.7. Market Research

6.5.8. Mathematical Models

6.5.8.1."Naive" Model

6.5.8.2. Moving Average Models

6.5.8.3. Exponential Smoothing Models

6.6. Dynamic Systems

6.6.1. Continuous Time Case

6.6.2. Discrete Time Case

6.6.3. Kalman Filter

7. Systematic General Problem Solving

7.1. Introduction

7.1.1. What Is a Problem and What Is a Solution?

7.1.2. Synthesis via Iterative Analysis

7.1.3. Abbreviated Version

7.2. Definition

7.2.1. General

7.2.2. Proper Characterization of the State

7.3. Objectives and Constraints Statement

7.3.1. Objectives

7.3.2. Setting Objectives

7.3.3. Constraints.

Contents note continued: 7.3.4. Boundary Conditions

7.3.5. Needs

7.4. Alternatives Generation

7.4.1. Environment

7.4.2. Assumptions

7.4.3. Causes

7.5. Analysis and Evaluation

7.5.1. Uncertainties

7.6. Selection

8. Creativity

8.1. Introduction

8.2. Creative Process

8.2.1. Logic

8.2.2. Idea Linking

8.2.3. Problem Solving

8.2.4. Free Association

8.3. Measuring Creativity

8.3.1. Traits

8.4. Types of Creativity

8.4.1. Something New

8.4.2.Combination

8.4.3. Extension

8.5. Stimulating Creativity

8.5.1. Idea Generation

8.5.2. Taking an Idea Census

8.5.3. Systems Engineering Approaches

8.5.4. Pure Creativity

8.5.5. Brainstorming and Similar

8.5.6. Other Techniques

8.6. Creativity and Organizations

8.6.1. Climate

9. General Problem Solving with Groups

9.1. Introduction

9.1.1. Mediation

9.1.2. Value Management

9.2. Participants

9.2.1. Facilitator

9.2.2. Recorder

9.2.3. Group Members

9.3. Facilitation.

Contents note continued: 9.3.1. Facilitator's Styles

9.3.2. Facilitator's Activities

9.3.2.1. Looking after the Process

9.3.2.2. Handling the People

9.3.2.3. Looking after the Surroundings

9.3.2.4. Agenda

9.3.3. Facilitator's Skills

9.4. Problem-Solving Steps

9.5. Groups versus Individuals

9.5.1. Building Consensus

10. Decision Making with Multiple Objectives

10.1. Introduction

10.1.1. Triple Bottom Line

10.2. Approaches and Examples

10.2.1. Approaches to Dealing with Multiple Objectives

10.2.2. Typical Applications

10.2.3. Selection of Plant and Equipment

10.2.4. Tender Evaluation and Assessment

10.2.5. Project Selection/End-Product Selection

10.2.6. Personal Applications

10.2.6.1. Vehicle Selection

10.2.6.2. Clothing Selection

10.2.6.3. Partner Selection

10.2.7. Employee Recruitment

10.2.7.1. Needs and Wants

10.2.8. Noninferior Solutions

10.3. Collective Decision Making

10.3.1. Social Welfare Function.

Contents note continued: 10.3.2. Public Involvement

11. Optimization

11.1. Introduction

11.1.1. Design

11.1.2. Outline

11.1.3. Optimization Techniques

11.1.4. Packages

11.2. Conventional Design

11.2.1. Example

11.2.2. Quantitative Formulation of Design

11.2.3. Conventional Design Approach

11.2.4. Alternative Computations

11.2.5. Formulation in Terms of T

11.3.Components of Optimization

11.3.1. Relationship to the Design Process

11.3.2. Alternative Terminology

11.3.3. Possible Approaches

11.3.4. Variables

11.3.5. Admissible Regions

11.4. Standard Forms

11.4.1. Standard Form SF1

11.4.2. Standard Form SF2

11.4.3. Standard Form SF3

11.4.4. Conversions between Standard Forms

11.4.4.1. Negative Variables

11.4.4.2. Maximization

11.4.4.3. Greater Than Inequalities

11.4.4.4. Equalities

11.4.4.5. Absolute Values

11.4.4.6. Inequalities

11.4.5. Algorithms

11.5. Elementary Optimization

11.5.1. Some Results of Calculus.

Contents note continued: 11.5.2. Equality Constraints

11.5.3. Lagrange Multipliers

11.5.4. Inequality Constraints

11.6. Linear Optimization

11.6.1. Example Linear Cases

11.6.1.1. Production Planning

11.6.1.2. Product Mix

11.6.1.3. Transportation

11.6.1.4. Assignment

11.6.1.5. Production Scheduling

11.6.1.6. Blending

11.6.2. LP Form

11.6.3. Multiple Objective Linear Programming

11.7. Nonlinear Programming

11.7.1. Unconstrained Case

11.7.1.1. Single Variable

11.7.1.2. Interval Elimination Methods

11.7.1.3. Function Approximation Methods

11.7.1.4. Multiple Variables

11.7.1.5. Gradient Method

11.7.1.6. Newton's Method

11.7.2. Constrained Case

11.7.2.1. Kuhn-Tucker Conditions

11.7.2.2. Quadratic Programming

11.7.2.3. Penalty Methods

11.8. Other Optimization Forms

12. Decision Approaches and Tools

12.1. Introduction

12.1.1. Terminology

12.2. Underlying Framework

12.3. Ranking Payoffs

12.3.1. Example.

Contents note continued: 12.3.1.1. Noncertainty Type (i)

12.3.1.2. Noncertainty Type (ii)

12.4. Decisions with Competition or Conflict

12.4.1. Game Examples

12.5. Decision Trees

12.5.1. Determining EMV

12.6. Bayes Theorem and Additional Information

12.7. Utility

12.7.1. Methods of Utility Function Determination

12.7.1.1. Certainty Equivalent Method

12.7.1.2. Example of Utility Function Determination

12.7.1.3. Human Behavior

12.7.2. Approximation for Utility Functions

Appendix A Sensitivity

Appendix B Surveys

B.1. Research

B.2. General Comment on Surveys

B.3. Questions

B.4. Some Suggestions

B.5. Survey Type

B.5.1. Personal Interviews

B.5.2. Telephone Interviews

B.5.3. Mail/Email Surveys

B.5.4. Panels

Appendix C Sampling

C.1. Random Sampling

C.2. Stratified Sampling

C.3. Other Sampling

C.4. Non-Reply

Appendix D Measurement and Scales

D.1. Nominal Scale

D.1.1. Multiple-Choice Questions

D.2. Ordinal Scale.

Contents note continued: D.2.1. Forced Ranking

D.2.2. Paired Comparison

D.2.3. Semantic Scale

D.3. Interval Scale

D.3.1. Bipolar Adjective

D.3.2. Other Approaches

D.4. Ratio Scale

D.4.1. Direct Quantification

D.4.2. Constant Sum Scale

D.5.Comments.