Month: December 2007

Wonderful OR Video

Bnet.com, which bills itself as “the go-to place for management” has a wonderful video on operations research, with Vijay Mehrotra from San Francisco State University. Vijay writes the ever-fascinating “Was it Something I Said” column in OR/MS Today, and his site has all his past columns.

In the video, Vijay gives three reasons why OR is more important:  faster computers, niche marketing and outsourcing.  I like these themes (though I generally go with faster computers, more and better data, and better algorithms):  they are appealing to the business-school audience and are, I think, important reasons why OR is being used more.

Vijay has lots of things going on: I highly recommend wandering through his site. My only disappointment is that the “occasional weblog” on his front page is broken: he is someone worth reading!

Operations Research and Fantasy Football

After a very successful year, my fantasy football teams are crashing and burning in the playoffs.  For those who do not know fantasy sports, fantasy football involves a group (8-12 people) drafting NFL players at the beginning of a season.  Each week, my team gets points based on the success (or lack thereof) of the players in their “real” games.  If my players get more points than my opponent’s, then I win.  After a regular season,  the best fantasy teams in the league then face off in the playoffs.  Some fantasy sports work a bit differently:  most fantasy baseball leagues collect statistics from the entire year and give points in the final year standings on the categories, without the head-to-head matchups.

One of my teams this year was the best I ever had.  I had the top three wide receivers in the league (Moss, Owens, and Edwards), the second best quarterback (Romo), a top running back (Addai), a great defense (Patriots), and a top kicker (Folk).  My tightend was not great, but that was the only weakness.   Over the regular season, I outscored the second best team in the league by an average of 30 points in a game (teams typically score 60-120 points in a game).  But, sure enough, the playoffs come around, my team goes cold, and I lose the first playoff round.   So now I am just struggling to get third place in the league.

I am not the best fantasy player around.  To be so would require much, much more time than my three-year-old son will allow me.  But I do try to use a bit of operations research thinking in my play.  Some (but not all) key decisions come in the initial draft.  Players are taken in turns in a serpentine fashion:  if there are 10 fantasy players, then the person with the first pick will next get the 20th pick;  the person with the 10th pick also gets the 11th pick).  Given the projected player values (which is the real key to the problem: data!), the “best pick” depends on what you expect others to do, and the relative value of the alternatives.  For instance, if you need a quarterback, and the best quarterback is worth, say, 280 points, with the next best being worth only 200 points (a huge difference), then you better pick that quarterback (unless you are absolutely sure that quarterback will be available when you pick next).  But if there are five quarterbacks in the 280 range, you can afford to wait, since it is more likely that one of them will still be available when you pick next.

Mike Fry, Andrew Lundberg (both of the University of Cincinnati) and Jeffrey Ohlmann (University of Iowa) analyzed this issue in depth in an article published in the new Journal of Quantitative Analysis in Sport.  Their work got a fair amount of press a few months ago, including a writeup in USA Today (sorry I missed it earlier:  New Zealand doesn’t cover football well!).  I just went through the article (while waiting for my son to wake up and enjoy Christmas), and it is terrific, going well beyond the obvious points.  I particularly liked the analysis of the value of each of the draft positions.  There is a view that drafting early is best, but the serpentine nature of the draft evens things out.  With the data they looked at, it is true that the first position is best, but the differences are quite slight, and the value is not monotonic in draft position.

At the end, though it comes down to player projections.  As the article quotes:

“If you value Ryan Leaf as the best quarterback, it’s going to tell you take him when it’s time to take a quarterback,” Ohlmann says. “If you give me bad projections, I’m going to give you some very bad advice.”

Crack, Cocaine and Operations Research

It might not be the most “Christmas-y” posting, but Al Blumstein of Carnegie Mellon (whose work I have discussed before) is quoted in the AP news coverage of the sentencing guidelines for crack versus powder cocaine.  In particular, he talks about the violence that crack created:

When crack first became popular, there was an increase in murders and other crimes associated with the drug. But the bloodshed was not necessarily the result of something inherent in crack.

Instead, most of that violence was typical for what happens when any illegal drug is introduced and drug dealers with guns compete for new markets, said Dr. Alfred Blumstein, a professor of urban systems and operations research at Carnegie-Mellon University.

This shows the sort of clear-thinking that OR engenders.  It also shows the value of having “Operations Research” in a professorial title:  it is important for more of our work to be associated with that term.

On that note, my very best wishes for the holidays to all!

OR in Popular Mechanics

When I was a kid, I loved the magazine Popular Mechanics.  In addition to articles on futuristic cars and planes, they always had articles on how things worked, and I seem to recall mechanically oriented projects that were always just outside my abilities.  As time went on, I realized that my mechanical abilities were limited indeed, so I moved on to the more cerebral Scientific American and mathematics.  These days, I am always amazed when I see Popular Mechanics in bookstores:  it is like a blast from the 60s.

Blake Nicholson of the University of Michigan wrote me to point out that a recent article in the magazine has a heavy OR focus.  In an article on Improving Air Travel,  one of the ten possible improvements, changing boarding strategies, is explicitly an OR approach.  A few of the other suggestions, including re-pricing landing slots to encourage better spreading of planes and the use of RFID in luggage tracking, are also OR approaches to air travel problems.

Out of touch for a few days

It is time for us to return from our year in New Zealand.  If you want to see a few hundred pictures of our year, you can check out our New Zealand blog. We are now deep into shipping and packing.  In a fit of insanity, we decided to stop off at Disneyland for a few days on the way back.  So no posts for a few days.  But I am sure my loyal readers will be pushing the cause of Operations Research at every opportunity!

ORSNZ Recap

I was at the OR Society of New Zealand conference last week, and failed to blog it.  I am so ashamed!  You can check out some pictures and other information about the conference at its site.  I was one of the speakers, so you can see me in action.

A highlight of the conference for me was the plenary session given by Gerard Cachon of Wharton, who is visiting the University of Auckland this year.  He gave a talk on the use of game theory in operations management.  I have been a little dubious of incredible fad for game theory in the analysis of supply chains.  The assumptions of game theory are pretty strong, and it is not clear that much of the analysis is giving any real insight.  Gerard is very aware of these limitations, and much of his talk was illustrating how difficult it is to conclude things from a game theoretic analysis.  For instance, there is often a huge problem with multiple equilibria, with game theory providing little guidance as to what the “right” equilibrium is.  After the talk, I felt much happier about game theory in OM:  as long as people are talking about the limitations, I can have much more confidence when they make conclusions.

Natashia Boland and Open Pit Mining

I am attending the Australian Society for Operational Research meeting in Melbourne.  I had thought Australia always had wonderful weather, but it is a gray, rainy day here.

The conference was opened with a talk by Natashia Boland, currently at the University of Melbourne (but I hear she is moving) who spoke on her experiences in using integer programming in practice.

The first part of the talk was about modeling excavation in open pit mines.  The goal in this problem is to dig out a mine in the best possible way.  The most critical constraint is, of course, that you can’t dig under stuff that is not already dug away.  There are other constraints on how much ore can be processed in a year, and on other operational requirements.  Of course, the amount of money that is at stake is huge:  these mines generate  hundreds  of millions of dollars of income, and require huge investments (see the wikipedia entry on the Bingham Canyon Mine for one example:  the pictured example is from Russia).  So planning the excavation is an important decision, even if most of the savings are just the time-value of money (getting $100 million this year rather than next is quite a difference!).  The integer programs that Natashia and her colleagues get are very large, so they have had to develop specialized branching rules, along with aggregation approaches to the decisions.

Natashia’s second example had to do with shipping planning for a fertilizer company.  The most striking point she made is that her approach still leaves an 8% gap between the upper bound (feasible solution) and lower bound.  This bound may be because the lower bound is poor (no possible solution can reach that value) but even 1 or 2% of an operation that costs a few hundred million dollars to run is a big deal.

Natashia gives very good, clear, inspirational talks.  The thing I like best is her choice of problems.  They are non-standard, but still involve huge amounts of money.  Every percentage point she saves would be enough to support an enormous research effort in integer programming.  If only she were to get a fraction of the savings!