Sample Chapters

Chapters

Below is a list of the MINDSET chapters. For chapters shown in blue, a brief summary is given below along with a link to download a pdf file of the chapter.

Deterministic Decision Making

1. Multi-Criteria Decision Making
2. Linear Programming - Maximization
3. Sensitivity Analysis
4. Linear Programming - Minimization
5. Integer Programming
6. Binary Programming
7. Location Problems
8. Shortest Path
9. Critical Path Method

Probabilistic Modeling

10. Decision Trees
11. Introduction to Basic Probability
12. False Positive/False Negatives
13. Binomial and Geometric Distributions
14. Poisson Distribution
15. Normal Distribution
16. Quality Control
17. Queuing Theory
18. PERT
19. Markov Chains

Last Updated on Saturday, 24 September 2011 14:21

Linear Programming Maximization - Product Mix

The chapter begins with an exploratory problem designed to introduce the concept of linear programming with an objective function to maximize profits by optimizing a company’s product mix. The problem context involves assembling two types of computers with different profit margins and labor requirements. Students are led through a graphical solution to a two decision variable problem involving two constraints. The second product mix example involves a detailed totally worked-out example involving the manufacture of skateboards. Students are shown step-by-step how to formulate and solve this two decision variable problem graphically. A third decision variable is then added to motivate the need for EXCEL to solve larger problems. Students are taught how to use SOLVER as standard add-in to EXCEL to solve linear programming problems. This section also discusses how to use the linear programming output to perform sensitivity analysis. There is also an optional section that discusses the Simplex algorithm that is the basis for computational solution of LP problems. The third example is a sports shoe company and focuses on interpretation of results. The text presents a fully formulated and solved problem involving six decision variables and six constraints. The emphasis is on interpreting the output from SOLVER and answering a variety of what-if questions.



Chapter 2 LP Maximization	(PDF)		941 Kb

Last Updated on Saturday, 24 September 2011 14:03

Binary 0-1 Problems

Binary programming is a form of integer programming. The word "binary" refers to the decision variables. When decision variables are binary, this means that they can only take on the values of either 0 or 1. That might seem overly restrictive, but there are many situations that can easily be modeled using binary decision variables. For example, the following decisions could be modeled with binary decision variables:
                  - Should we located a new automobile dealership at this location?
                  - Should I choose to apply to this college?
                  - Should I invest in this stock?



Chapter 6 Binary Problems	(PDF)		941 Kb

Last Updated on Saturday, 24 September 2011 14:20

Decision Trees: Auto Insurance

We all make decisions everyday in our lives that involve uncertainty. Decision Trees is the first chapter in the Probabilistic material and introduces the concept of making decisions under uncertainty and risk. The decision tree methodology involves accounting for every possible decision and random event. The best alternative generally maximizes the expected value of profit or minimized the expected cost, however, other non-financial variables are also considered. Expected value does not also capture an individual’s risk tolerance. This risk aversion is the foundation for the insurance industry. The final problem in this chapter follows Jee Min a high school junior as he tries to determine how much collision insurance he needs.



Chapter 10 Decision Trees	(PDF)		941 Kb

Last Updated on Saturday, 24 September 2011 14:07