Closed Loop Supply Chains

There is a new paper on the OR Forum by Dan Guide and Luk Van Wassenhove that looks at the research trajectory of “Closed Loops Supply Chains”.  Closed loop supply chains are supply chains where there is at least as much interest in getting things from the customer to the supplier as vice versa.  Sometimes the drive for this is environmental (think European electronics laws to try to reduce metals in the refuse system) and some is economic (think of a printer manufacturer getting back used cartridges to try to cut down on the refill market, or firms that restore used items for further sale).  Luk and Dan’s paper is a nice, personal, view of the research that has gone on in the last years.

For about eight years (1997-2005), I headed up the Carnegie Bosch Institute.  Part of what we did was sponsor conferences and workshops on emerging topics in international management.  One of our success stories was early support for closed loop supply chains (or reverse logistics).  I am really pleased to see how the field has developed.

I’m ready for my close-up Mr. DeMille, the Operations Research Version

If all goes according to plan, the members of INFORMS will receive an email over the next two days.  The email outlines some reasons why you should attend the upcoming INFORMS Practice Meeting (note that you need to register by April 1 in order to get a discount on the registration fee).  Part of the email is a video featuring … me!  In my two minute schtick, I try to give you some reasons why I like the INFORMS Practice conference so much.

I found the video really hard to do.  I vacillated between spontaneous and rigid.  When spontaneous, I had enough verbal tics that it was unwatchable.  “I, um, really like the INFORMS Practice Conference, you know, um, because, um…”  Arghh!  The other extreme made me look as though madmen had captured my loved ones and were forcing me to to read their manifesto against my will.  So I tried to split the difference in the final video.  Perhaps now it looks like I am being forced to read the manifesto with a verbal tic.  As my wife said “It was fine, but you are no actor”.  Despite that, you really should think about attending the INFORMS Practice Conference:  it is inspiring to see what our field does in the real world.

If you can’t wait for the email, you can check it out here.

Much more on the NCAA Tournament

In the hyper-competitive world of operations research blogging, needing to teach a class can put you hopelessly behind.  The Blog-OR-sphere is abuzz with pointers to the CNN article on computer models for predicting success in the upcoming NCAA tournament featuring Joel Sokol (see the video here).  See the blog entry at Punk Rock Operations Research as well as previous entries by me and Laura on LRMC (Logistic Regression/Markov Chain) and Laura’s article on the work of Sheldon Jacobson and Doug King.  We previously saw Sheldon in articles on predicting the US Presidential election.

Getting back to the CNN article, it is a good illustration on how hard it is to write about models:

At their cores, the computer models all operate like question machines, said Jeff Sagarin, who has been doing computer ratings for USA Today since 1985.

Different people come up with different brackets because they’re asking different questions.

Sagarin’s equations ask three questions: “Who did you play, where did you play and what was the result of each specific game?” The computer keeps repeating those questions in an “infinite loop” until it comes up with a solid answer, he said.

Sagarin has arranged the formula as such partly because he thinks home-court advantage is a big deal in college basketball.

Other models ask different questions or give the questions different weights. Sokol, of Georgia Tech, for example, cares more about the win-margin than where the game was played.

Well… kinda.  It is not that Joel has a philosophical belief in win-margin versus home court.  It is simply that his models include win-margin and the resulting predictions are more accurate because they do so.  Joel didn’t go in and say “Win margin is more important than home court”:  it is the accuracy of the resulting predictions that gives that result.  Some of his models don’t include win margin at all!

I also loved the quote:

Dan Shanoff, who blogs on sports at danshanoff.com, said gut feeling is more important than statistics, but taking a look at the numbers can never hurt.

Followup question:  “So how do you know that gut feeling is more important than statistics, Dan?”.  Reponse (presumably): “Well, it is really my gut feeling, you know, since I really haven’t looked at the numbers”. [Followup added:  Dan isn’t sure he really said what he was quoted as saying.]

Be sure to check out Laura and me, and any other OR people twittering the tournament with tag #ncaa-or, starting noon Thursday.

Tweeting the Tournament

Following up on a post from Punk Rock Operations Research, let’s use a hashtag for OR people twittering about the tournament.  I think “#ncaa-or” should work nicely.  Follow that tag at http://search.twitter.com or directly here.  And start your tweets with #ncaa-or if you want to be part of the group. Thanks to twitterers hakmem and nanoturkiye for instructions on how to set this up!

Are you ready for some College Basketball?

Joel Sokol, Paul Kvam, and George Nemhauser have a ranking called LRMC (Logistic Regression/Markov Chain) for college basketball.  This weekend is when the NCAA selects teams for its championship.  You can check out the current rankings to see whether your favorite team deserves to be in the tournament.  And, once the bracket is published, LMRC provides a guideline for predicting who will win each game.  In the past, LRMC has done very well, but I am still going to go with Pittsburgh over UNC, despite the rankings.

Bernie Madoff and Data Visualization

If you are like most people, when you hear of Bernie Madoff’s Ponzi scheme ripping off investors to the tune of $50 billion, you might think “Oh those poor investors”, or perhaps “Just the rich ripping off the other rich”.  If you do research in a business school, you might wonder about the institutional controls that allowed for such a long-term scam.  But if you are in operations research, you probably think:  what a great source of data!  I wonder what I can do with that?

A couple of the results of the last question (thanks Bryan!):  GeoCommons (“Visual Analytics through Maps”) have a very cool map of Madoff’s investors.  While the map doesn’t contain any information that is not part of the 162 page listing of investors, the visualization leads to lots of interesting questions:  why so much around Denver?  Why so little in Asia?  If there was one person in Auckland, New Zealand involved, is it surprising it was in Parnell?  And who was that guy in Northern Canada who got ripped off, eh?  (The latter appears to be a misplacement:  there is a Lac Carre outside of Montreal).  Other maps are here and here.

Network from The Network Thinker
Network from The Network Thinker

Even better is the growing analysis of the social network involved in the Madoff scam.  The Network Thinker has a great graph pointing out who invested with whom (there is also an interactive map).  This leads to all sorts of graph theoretic questions:  what is the longest path in the graph?  What do components of the graph (minus Madoff) correspond to?  Are there cliques or near-cliques in the graph?

This is great data that I am sure will be used in countless dissertations over the next years.  It probably wasn’t worth $50 billion to get that data, but we might as well use it now that we have it.

Twitter for Operations Research

There are tons of “Web 2.0” (or 2.1, or whatever) applications out there that I don’t really understand how to use.  I know I have 154 connections on LinkedIn, but I don’t know why.  I have received a flurry of Plaxo requests, but I can’t tell if that is more or less useful than LinkedIn.  I’ve played with wikis and with wikipedia (before deciding I didn’t want to spend my life arguing with non-OR people about the operations research page).  And, of course, I have a blog, and use lots of software to bring together various RSS feeds.  But,except for the last, I am not sure if what I do is useful, or if it is just messing about to no purpose.

One system that intrigues me is Twitter.  Every few months I tell myself that I should use Twitter more, and add a bunch of tweets for a day or so before lapsing back.  Unlike the blog, I never found a voice for Twitter.  I’m trying again, this time putting my tweets in a sidebar on the main page of this blog (the sidebar is getting far too messy but maybe one more thing can fit in!).  But why?  About the most I can say is that this might give some people an idea of what a university professor does and perhaps give an outlet for quicker thoughts about the OR world than the blog does.  I see people like Wil Wheaton and Steve Baker and I see them using Twitter in interesting ways.   Could I find a similar path?

Anyone else want to point the way on how Twitter can improve the world of operations research?

Back at the IMA

I am at the Institute for Mathematics and its Applications at the University of Minnesota.  This brings back very fond memories.  I was a postdoc here 21 years ago at the start of my career when they had a Special Year on Applied Combinatorics.  As I recall there were 10 postdocs that year:  nine combinatorialists and me who was trained in operations research.  The combinatorialists were all scary smart and many (including Bernd Sturmfels) went on to sparkling careers.   Doing my two postdocs (in addition to the IMA, I spent a year in Bonn, Germany at Prof. Korte’s Institute) was the best thing I have done in my career.  The postdoctoral time gave me the opportunity to move past my doctoral work and to start new research directions even before I took a permanent position.  And, given I met my wife during my postdoc in Bonn, the social aspects were also wonderful.

I am speaking tonight in the IMA’s Math Matters series.  My topic is “Sports Scheduling and the Practice of Operations Research”.   The talk is open to everyone,  so if you are in the Minneapolis area, feel free to come on by!  There has already been some press on this.

Kindle and Math

Added January 6 2012.  Note that this post refers to the kindle circa 2009.  See this discussion on reddit for more recent (late 2011) information.  Unfortunately I no longer use a Kindle so I cannot provide any updated information.

The new Kindle from Amazon is out, and it is receiving a lot of press.  Aurelie Thiele points out the funny pricing of Amazon.  Of course, none of this is open in any sense of the word:  Amazon wants to keep control here.  I bought a Kindle for a trip I am on, and I really enjoy it so far, but I really bought it for research reasons: for reasons I’ll make clear in an upcoming post, I really need to travel with a large amount of technical material, so I thought this would be a good thing.  But how to get math on the Kindle?  My friend and sometime co-author Stan Zin has been working on this and writes:

I converted a pdf file of a paper with lots of math into a
Kindle-readable azw file (using @free.kindle.com).  It can’t handle the math very well, especially multi-line formulas.  Basically the math is completely garbled in translation.  I also tried to first convert pdf to a graphics file (eg, jpg, gif, png) then convert that to an azw file.  Now it’s unreadable because of scale.  The Kindle version doesn’t seem to be zoomable, and so is also unreadable.  Since Kindle’s azw format will handle Greek letters, as well as subscripts and superscripts, it would seem to have all the necessary components for generating complicated math.  But the conversion step from pdf doesn’t seem to be the way to go.  I was wondering if your OR blog readers might take this as a challenge.  How hard could it be for a hacker to create a LaTex2azw program?  I think there would be a big demand if it worked reasonably well.

What do you think?  Is there a way to get math on the Kindle?