# Category Archives: Teaching

## (Unexpected) Lessons Learned From a Prescriptive Analytics Course Refresh

I’ve been teaching the core prescriptive analytics course in our MBA program since 2005, and it was once again time for a major refresh/improvement. Therefore, this past December I went for it, and then proceeded to ask myself the following question over and over during the first half of the spring semester: “Why did you do this to yourself?” It was grueling. It was hard. It was overwhelming. However, when I look at the end result, I am really proud of it. It’s far from perfect, but I collected tons of feedback from my students (THANK YOU!) and I’m already polishing it for the next iteration (that one will be close to spotless). The goal of this post is to share a portion of the feedback I received, which turned out to contain some unexpected surprises.

To give you some context, I improved the existing homework problems, Excel files, and lecture notes (everything now is in PDFs created with LaTeX; no Powerpoint anymore!). I also created a lot of new content (including a hands-on Lego exercise and a case study I developed from scratch), especially when it comes to decision-making under uncertainty. All in all, I’d say 50% of the course is new material, and the remaining 50% is a much better version of the previous material.

Here are the lecture themes (unless otherwise noted, each lecture is a 2-hour session; there are 2 sessions per week). An asterisk * indicates a brand new lecture:

• Lecture 1: Introduction to Optimization: hands-on Lego exercise, investment, scheduling.
• Lecture 2: Mixing and Carryovers: blending, cash flow, production and inventory planning.
• Lecture 3: Networks: assignment, transshipment, shortest path.
• Lecture 4: Integer and Binary Variables: budget allocation, logical conditions, covering, fixed costs.
• Lecture 5: Sensitivity Analysis: shadow prices and reduced costs.
• MIDTERM EXAM
• Lecture 6*: Optimization Under Uncertainty: two-stage stochastic linear programming.
• Lecture 7*: Decision Analysis and Decision Trees (incl. EVPI, EVSI, risk profiles, Bayes’ Rule).
• Lecture 8*: Sequential Decision Processes: stochastic dynamic programming.
• Lecture 9* (two sessions): Monte Carlo Simulation.
• Lecture 10*: Case Discussion.
• FINAL EXAM

On my final exam, I had a question asking students to name their favorite and least favorite lecture. I wanted to see where the home run was and where the big fail was. I had a total of 76 students, and here is the final tally:

You’ll notice that the columns don’t add up to 76 as they should. Some people provided two favorites, and some people did not indicate a least favorite lecture. Here’s what I infer from the table above. (Let me know if you see something I didn’t!)

• Networks as the clear “home run” lecture: I would never have guessed that in a million years! This was so surprising to me. Some of the comments mentioned the visual aspect of it as an attractiveness. I can see that, but still…
• Stochastic DP as the clear “big fail” lecture: I can actually see that because I wasn’t very fond of the lecture myself after I taught it. It looks OK on paper, but the classroom feeling I got from it was lukewarm. I’m thinking of replacing it with a second case study to take place after the first four lectures.
• Two-stage stochastic LP as pretty much another big fail: This one I attribute to a fail on my part. The material in there is pretty good. It’s just a matter of my doing a better job motivating and explaining it (the written feedback points to its being hard to follow). I’ll keep it for sure and refine the delivery. (Two people actually loved it! :-)
• Simulation as a bit of a polarizing topic: I honestly thought the simulation class was going to be a big home run. The 11 votes in the right column are likely my fault again (based on feedback). I fully believe in the quality of the content, but I need to do a better job connecting it with the statistics course that precedes mine (I assumed too much of the students).
• Beloved decision trees: I had a lot of fun putting together and delivering the decision tree lecture, but I didn’t expect it would get so many “fave” votes. Being a new lecture, there were some hiccups that I can easily fix (the same being true for lectures 6, 8, and 9), and seeing how successful it already was gives me even more motivation.
• Case study: I had never written a full-length case study before, but got excited about doing so after an instructional session organized for the faculty at our school. Despite liking the case on paper (it combines marketing and optimization aspects), I was a bit anxious before the actual discussion took place. My students did an amazing job and performed way beyond my expectations. After seeing where they went and what issues they brought up, I feel like I can polish the case to make it even better.
• Lego exercise: Another pleasant surprise. I was worried the Lego exercise might be seen as too simple or silly. I was wrong! The feedback indicated it worked well as a motivational tool, a visual aid, and a good ice breaker (it’s the very first thing I do in Lecture 1).

I’m pretty happy overall and really enjoyed what I learned from this fave/least fave question. It can be pretty informative. Do you have any other creative ways to elicit student feedback that you find particularly helpful? If so, please share them in the comments below.

1 Comment

Filed under Decision Trees, Motivation, Network Flows, Teaching, Tips and Tricks

## Portuguese Pronunciation and Language Tips

I’ll be taking a group of 34 MBA students on an international business immersion trip to my native Brazil this Spring. We’ll be visiting about a dozen companies in the cities of São Paulo and Rio de Janeiro. This is an initiative created by the awesome Center for International Business Education and Research (CIBER) at the University of Miami.

I’d like my students to be able to pronounce some of the main sounds in Portuguese correctly because I know Brazilians pay attention and really enjoy when foreigners make an effort to say things properly. Therefore, I created a video in which I go over what I consider to be some of the most important things to know when speaking Portuguese (there are others, but I didn’t want the video to be too long).

Moreover, the 2016 Olympic Games are coming, so I figured these tips could be useful for a larger audience as well. I wish American sports casters would watch this video because they murdered the pronunciation of everything during the World Cup in 2014.

Bonus material: My daughter, Lavinia Lilith, a.k.a. #LLCoolBaby, makes a short appearance at around the halfway mark.

Enjoy!

Filed under Brazil, People, Teaching, Tips and Tricks, Travel, Videos, YouTube

## Improving a Homework Problem from Ragsdale’s Textbook

UPDATE (1/15/2013): Cliff Ragsdale was kind enough to include the modification I describe below in the 7th edition of his book (it’s now problem 32 in Chapter 6). He even named a character after me! Thanks, Cliff!

When I teach the OR class to MBA students, I adopt Cliff Ragsdale’s textbook entitled “Spreadsheet Modeling and Decision Analysis“, which is now in its sixth edition. I like this book and I’m used to teaching with it. In addition, it has a large and diverse collection of interesting exercises/problems that I use both as homework problems and as inspiration for exam questions.

One of my favorite problems to assign as homework is problem number 30 in the Integer Linear Programming chapter (Chapter 6). (This number refers to the 6th edition of the book; in the 5th edition it’s problem number 29, and in the 4th edition it’s problem number 26.) Here’s the statement:

The emergency services coordinator of Clarke County is interested in locating the county’s two ambulances to maximize the number of residents that can be reached within four minutes in emergency situations. The county is divided into five regions, and the average times required to travel from one region to the next are summarized in the following table:

The population in regions 1, 2, 3, 4, and 5 are estimated as 45,000,  65,000,  28,000,  52,000, and 43,000, respectively. In which two regions should the ambulances be placed?

I love this problem. It exercises important concepts and unearths many misconceptions. It’s challenging, but not impossible, and it forces students to think about connecting distinct—albeit related—sets of variables; a common omission in models created by novice modelers. BUT, in its present form, in my humble opinion, it falls short of the masterpiece it can be. There are two main issues with the current version of this problem (think about it for a while and you’ll see what I mean):

1. It’s easy for students to eyeball an optimal solution. So they come back to my office and say: “I don’t know what the point of this problem is; the answer is obviously equal to …” Many of them don’t even try to create a math model.
2. Even if you model it incorrectly, that is, by choosing the wrong variables which will end up double-counting the number of people covered by the ambulances, the solution that you get is still equal to the correct solution. So when I take points off for the incorrect model, the students come back and say “But I got the right answer!”

After a few years of facing these issues, I decided I had had enough. So I changed the problem data to achieve the following (“evil”) goals:

1. It’s not as easy to eyeball an optimal solution as it was before.
2. If you write a model assuming every region has to be covered (which is not a requirement to begin with), you’ll get an infeasible model. In the original case, this doesn’t happen. I didn’t like that because this isn’t an explicit assumption and many students would add it in.
3. If you pick the wrong set of variables and double-count the number of people covered, you’ll end up with an incorrect (sub-optimal) solution.

These improvements are obtained by adding a sixth region, changing the table of distances, and changing the population numbers as follows:

The new population numbers (in 1000’s) for regions 1 through 6 are, respectively, 21, 35, 15, 60, 20, and 37.

I am now much happier with this problem and my students are getting a lot more out of it (I think). At least I can tell you one thing: they’re spending a lot more time thinking about it and asking me intelligent questions. Isn’t that the whole purpose of homework? Maybe they hate me a bit more now, but I don’t mind practicing some tough love.

Feel free to use my modification if you wish. I’d love to see it included in the 7th edition of Cliff’s book.

Note to instructors: if you want to have the solution to the new version of the problem, including the Excel model, just drop me a line: tallys at miami dot edu.

Note to students: to preserve the usefulness of this problem, I cannot provide you with the solution, but if you become an MBA student at the University of Miami, I’ll give you some hints.