Category: INFORMS

The Appeal of Operations Research and Sports

For a more recent comment on MLB scheduling and the Stephensons see my response to the 30 for 30 video.

The relationship between operations research and sports is one topic that I return to often on this site.    This is not surprising:  I am co-owner of a small sports scheduling company that provides schedules to Major League Baseball and their umpires, to many college-level conferences, and even to my local kids soccer league.  Sports has also been a big part of my research career.  Checking my vita, I see that about 30% of my journal or book chapter papers are on sports or games, and almost 50% of my competitive conference publications are in those fields.  Twenty years ago, my advisor, Don Ratliff, when looking over my somewhat eclectic vita at the time (everything from polymatroidal flows to voting systems to optimization implementation) told me that while it was great to work in lots of fields, it is important to be known for something.  To the extent that I am known for something at this point, it is either for online stuff like this blog and or-exchange, or for my part in the great increase of operations research in sports, and sports scheduling in particular.

This started, as most things in life often do, by accident.  I was talking to one of my MBA students after class (I was younger then, and childless, so I generally took my class out for drinks a couple times a semester after class) and it turned out he worked for the Pittsburgh Pirates (the local baseball team).  We started discussing how the baseball schedule was created, and I mentioned that I thought the operations research techniques I was teaching (like integer programming) might be useful in creating the schedule.  Next thing I know, I get a call from Doug Bureman, who had recently worked for the Pirates and was embarking on a consulting career.  Doug knew a lot about what Major League Baseball might look for in a schedule, and thought we would make a good team in putting together a schedule.  That was in 1996.  It took until 2005 for MLB to accept one of schedules for play.  Why the wait?  It turned out that the incumbent schedulers, Henry and Holly Stephenson were very good at what they did.  And, at the time, the understanding of how to create good schedules didn’t go much beyond work on on to minimize breaks (consecutive home games or away games) in schedules, work done by de Werra and a few others.  Over the decade from 1996-2005, we learned things about what does work and what doesn’t work in sports scheduling, so we got better on the algorithmic side.  But even more important was the vast increase in speed in solving linear and integer programs.  Between improvements in codes like CPLEX and increases in the speed of computers, my models were solving millions of times faster in 2005 than they did in 1996.  So finally we were able to create very good schedules quickly and predictably.

In those intervening years, I didn’t spend all of my time on Major League Baseball of course.  I hooked up with George Nemhauser, and we scheduled Atlantic Coast Conference basketball for years.  George and I co-advised a great doctoral student, Kelly Easton, who worked with us after graduation and began doing more and more scheduling, particularly after we combined the baseball activities (with Doug) and the college stuff (with George).

After fifteen years, I still find the area of sports scheduling fascinating.  Patricia Randall, in a recent blog post (part of the INFORMS Monthly Blog Challenge, as is this post) addressed the question on why sports is such a popular area of application.  She points to the way many of us know at least something about sports:

I think the answer lies in the accessibility of the data and results of a sports application of OR. Often only a handful of people know enough about an OR problem to be able to fully understand the problem’s data and judge the quality of potential solutions. For instance, in an airline’s crew scheduling problem, few people may be able to look at a sequence of flights and immediately realize the sequence won’t work because it exceeds the crew’s available duty hours or the plane’s fuel capacity. The group of people who do have this expertise are probably heavily involved in the airline industry. It’s unlikely that an outsider could come in and immediately understand the intricacies  of the problem and its solution.

But many people, of all ages and occupations, are sports fans. They are familiar with the rules of various sports, the teams that comprise a professional league, and the major players or superstars. This working knowledge of sports makes it easier to understand the data that would go into an optimization model as well as analyze the solutions it produces.

I agree that this is a big reason for popularity. When I give a sports scheduling talk, I know I can simply put up the schedule of the local team, and the audience will be immediately engaged and interested in how it was put together. In fact, the hard part is to get people to stop talking about the schedule so I can get on talking about Benders’ approaches or large scale local search or whatever is the real content of my talk.

But let me add to Patricia’s comments: there are lots of reasons why sports is so popular in OR (or at least for me).

First, we shouldn’t ignore the fact that sports is big business. Forbes puts the value of the teams of Major League Baseball to be over $15 billion, with the Yankees alone worth $1.7 billion. With values like that, it is not surprising that there is interest in using data to make better decisions. Lots of sports leagues around the world also have high economic effects, making the overall sports economy a significant part of the overall economy.

Second, there are a tremendous number of issues in sports, making it applicable and of interest to a wide variety of researchers. I do essentially all my work in scheduling, but there are lots of other areas of research. If you check out the MIT Sports Analytics conference, you can see the range of topics covered. By covering statistics, optimization, marketing, economics, competition and lots of other areas, sports can attract interest from a variety of perspectives, making it richer and more interesting.

A third reason that sports has a strong appeal, at least in my subarea of scheduling, is the close match between what can be solved and what needs to be solved. For some problems, we can solve far larger problems than would routinely occur in practice. An example of this might be the Traveling Salesman Problem. Are there real instances of the TSP that people want to solve to optimality that cannot be solved by Concorde? We have advanced so far in solving the problem, that the vast majority of practical applications are now handled. Conversely, there are problems where our ability to solve problems is dwarfed by the size of problem that occurs in practice. We would like to understand, say, optimal poker play for Texas Hold’em (a game where each player works with seven cards, five of them in common with other players). Current research is on Rhode Island holdem, where there are three cards and strong limitations on betting strategy. We are a long way from optimal poker play.

Sports scheduling is right in the middle. A decade ago, my coauthors and I created a problem called the Traveling Tournament Problem. This problem abstracts out the key issues of baseball scheduling but provides instances of any size. The current state of the art can solve the 10 team instances to optimality, but cannot solve the 12 team instances. There are lots of sports scheduling problems where 10-20 teams is challenging. Many real sports leagues are, of course, also in the 10-20 team range. This confluence of theoretical challenge and practical interest clearly adds to the research enthusiasm in the area.

Finally, there is an immediacy and directness of sports scheduling that makes it personally rewarding. In much of what I do, waiting is a big aspect: I need to wait a year or two for a paper to be accepted, or for a research agenda to come to fruition. It is gratifying to see people play sports, whether it is my son in his kid’s soccer game, or Derek Jeter in Yankee Stadium, and know not only are they there because programs on my computer told them to be, but that the time from scheduling to play is measured in months or weeks.

This entry is part of the March INFORMS Blog Challenge on Operations Research and Sports.

Great Way to Get to The INFORMS Conference on Business Analytics and Operations Research

I am very much looking forward to attending this year’s INFORMS Conference on Business Analytics and Operations Research (formally the INFORMS Practice Conference).  I am a judge for the Edelmans, so I will be spending Monday watching the presentations and asking tough questions (“Wow, did you really save $200 million?  That’s so cool!”).  I’ll also be attending some of the Technology Workshops on Sunday, and will attend other presentations on Tuesday.

Over the last few years, I have scrabbled together some funds to support sending some of the Tepper MBA students to the conference (thanks Tepper Administration for all of your support!), and they always come back raving about the conference and the field.  I expect this year to be no different:  we’ll have four students in our business analytics track (at least!) at the conference.

Two of the students will be attending the Professional Colloquium, a day-long program for Masters and PhD students who are transitioning into real-world careers.   I always worry when I suggest this to MBAs since the professional skills and insight into organizations that the day provides are the same skills an MBA provides (and which are more commonly lacking in normal masters programs in operations research).  Will they get enough out of the day? But every MBA who has attended the Colloquium has loved it:  the speakers provide insights into success from the perspective of operations research/business analytics professionals.  For many of the students who have attended, this is a life-changing experience.  I see that one of my students from a couple of years ago thinks enough of this to be part of this year’s organizing committee!

Whether you are a business analytics-oriented MBA, a Masters of OR or IE, or a Doctoral student (or a recent graduate in any of these areas), I can’t recommend the program highly enough.  And, while the registration fee of $375 might not seem cheap, it really is a steal, since it includes participation in the full conference as well as the Colloquium.  There are some limited support funds from the Colloquium committee, but this is the sort of activity that your school really should be supporting (and even if not, this is a great investment in your career).

Applications are due March 25, so get going if you want to be part of this!

Programming versus Optimization

Renaming is a powerful way to show change.  Recently, I came across two colleagues who changed names.  Once did the equivalent of changing names from Michael to Michelle, signifying some very significant life changes.  The other went from a name like John Smith to Luigi Backtrend (real names changed to protect the innocent!), wanting to make himself more unique and visible to online searches.   Name changes like this can affect a life and career:  no one listened to the song stylings of Arnold Dorsey until he became Englebert Humperdinck, and Marion Morrison could not possibly be the hero of a western, but he could, renamed as John Wayne.

Somehow I completely missed the changes that the Mathematical Programming Society has undergone in this regard.  Last year, the forty-year-old MPS became the Mathematical Optimization Society.  At an age when many men are buying sports cars and experimenting with extramarital affairs, the MPS decided a name change would scratch its mid-life crisis itch.

While I am not enthusiastic about the change, I am sympathetic to the need.  The word “programming” in mathematical programming (and linear programming, integer programming, dynamic programming and so on) does not match up with the current use of “programming” to mean, almost exclusively, the programming of computers.  Back in the 40s and 50s, “programming” could be used for any sort of planning, so “linear programming” made sense: it was a method for determining (planning or programming) the maximum of a linear function over linear constraints.  The word “program” is still used in many contexts (“television program”, “conference program”, and so on) but in much of our world, “program” now means one thing: a computer program.  So the meaning of “linear programming” is no longer self-evident. “Mathematical programming” could now be misinterpreted as creating computer programs for mathematics, which is not quite what the field defines it as.

The diagram shows how often the phrases “linear programming” and “computer program” appear in books, through the Google Books ngram system.  I am surprised that linear programming does that well (and it dominates “computer programming”):  it is a term with a great history.  But “computer program” is certainly more common these days, and I suspect there are many, many instances where program is used without a qualifier to mean “computer program”.

“Optimization” has, at least so far, kept a meaning of “finding the best value”, though I hear my students (and researchers in meta-heuristics) refer to “more optimal” solutions so often that I fear it too is losing its meaning.  So “Mathematical Optimization” is a bit more self-evident.   It is, however, not a term that has been used a lot.  The same ngram system does not show anywhere near as much use of “mathematical optimization” as “mathematical programming”.  And while the ngrams are only for books, Google search shows a five to one advantage for “mathematical programming” over “mathematical optimization”, while the ratio is twenty to one in Google Scholar.

But even our field did not uniformly adopt “programming” over “optimization”.  It is, after all “combinatorial optimization” not “combinatorial programming”.  So there is some historical precedent for the use of “optimization”.

While I hate the idea of downgrading the word “programming” in our field (it is not just “computer programming”!), I understand why MPS/MOS decided to be proactive on this front.  And I appreciate they way they did it quickly, seemingly without the endless hand-wringing of those of us in operations research/management science/decision optimization/prescriptive analytics/advanced business analytics.  There may come a day when the Institute for Operations Research and the Management Sciences (INFORMS) changes its name:  I can’t believe it will be done as easily as MPS/MOS has done it.

Finding Love Optimally

Like many in operations research, my research interests often creep over into my everyday life. Since I work on scheduling issues, I get particularly concerned with everyday scheduling, to the consternation of my friends and family (“We should have left six minutes ago: transportation is now on the critical path!”). This was particularly true when I was a doctoral student when, by academic design, I was living and breathing operations research 24 hours a day.

I was a doctoral student from ages 22 to 27 (age will be relevant shortly), and like many in that age group, I was quite concerned with finding a partner with whom to spend the rest of my life. Having decided on certain parameters for such a partner (female, breathing, etc.), I began to think about how I should optimally find a wife. In one of my classes, it hit me that the problem has been studied: it is the Secretary Problem! I had a position to fill (secretary, wife, what’s the difference?), a series of applicants, and my goal was to pick the best applicant for the position.

Fortunately, there is quite a literature on the Secretary Problem (for a very nice summary of results, see this site, from which I also got the background to the graphic for this entry), and there are a number of surprising results. The most surprising is that it is possible to find the best secretary with any reasonable probability at all. The hard part is that each candidate is considered one at a time, and an immediate decision must be made to accept or reject the candidate. You can’t go back and say “You know, I think you are the cat’s meow after all”. This matched up with my empirical experience in dating. Further, at each step, you only know if the current candidate is the best of the ones you have seen: candidates do not come either with objective values or with certifications of perfection, again matching empirical observations. You can only compare them with what you have sampled.

Despite these handicaps, if you know how many candidates there are, there is a simple rule to maximize the chance of finding the best mate: sample the first K candidates without selecting any of them, and then take the first subsequent candidate who is the best of all you have seen. K depends on N, the total number of candidates you will see. As N gets big, K moves toward 1/e times N, where e is 2.71…. So sample 36.9% of the candidates, then take the first candidate who is the best you have seen. This gives a 36.9% chance of ending up with Ms (in my case) Right.

One problem here: I didn’t know what N is. How many eligible women will I meet? Fortunately, the next class covered that topic. If you don’t know what N is but know that you will be doing this over a finite amount of time T, then it is OK to replace this with a time cutoff rule: simply take the first candidate after 36.9% of the time (technically, you use 36.9% of the cumulative distribution, but I assumed a uniform distribution of candidate arrivals). OK, I figured, people are generally useless at 40 (so I thought then: the 50-year-old-me would like to argue with that assumption), and start this matching process at about 18 (some seem to start earlier, but they may be playing a different game), so, taking 36.9% of the 22 year gap gives an age of 26.11. That was my age! By a great coincidence, operations research had taught me what to do at exactly the time I needed to do that.

Redoubling my efforts, I proceeded to sample the candidate pool (recognizing the odds were against me: there is still only a 36.9% chance of finding Ms Right) when lo and behold. I met Her: the woman who was better than every previous candidate. I didn’t know if she was Perfect (the assumptions of the model don’t allow me to determine that), but there was no doubt that she met the qualifications for this step of the algorithm. So I proposed.

And she turned me down.

And that is when I realized why it is called the Secretary Problem, and not the Fiancee Problem (though Merrill Flood proposed the problem under that name). Secretaries have applied for a job and, presumably, will take the job if offered. Potential mates, on the other hand, are also trying to determine their best match through their own Secretary Problem. In order for Ms Right to choose me, I had to be Mr. Right to her! And then things get much more complicated. What if I was meeting women in their sampling phase? It did seem that some people were very enthusiastic about having long sampling phases, and none of them would be accepting me, no matter how good a match they would be for me. And even the cutoff of 36.9% looks wrong in this case. In order to have a hope of matching up at all in this “Dual Secretary Problem”, it looked like I should have had a much earlier cutoff, and in fact, it seemed unlikely there was a good rule at all!

I was chagrined that operations research did not help me solve my matching problem. I had made one of the big mistakes of practical operations research: I did not carefully examine the assumptions of my model to determine applicability.

Downcast, I graduated with my doctorate, resolving to marry myself to integer programming. I embarked on a postdoc to Germany.

There, I walked into a bar, fell in love with a beautiful woman, moved in together three weeks later, invited her to live in the United States “for a while”, married her six years after that, and had a beautiful son with her six years ago. I am not sure what optimization model led me down that path, but I think I am very happy with the result.

Some details of this story have been changed to protect me from even more embarrassment. This post is part of the February INFORMS Blog Challenge.

How Operations Research Helps Me Understand Politics and Voting

Over the years, operations research, politics, and voting have intersected often for me. Going back almost 25 years now, I have done research on voting systems. I have blogged on elections, and written about predicting results and presenting results. I have written about political leaders who were trained in operations research, and even countries run on O.R. principles.

Over time, I have been both elated and disillusioned by politics at both the national and local scale. I use what I know about elections when I run committees, and get very frustrated by others running committees without an understanding of the basics of voting theory.

While I will not claim to be an expert on how theory and practice interact in politics and elections, I do have some insights. Many of these are well known to those in voting theory, but some are a little idiosyncratic. Perhaps we can have an election afterward on which is the most useful.

  1. When there are more than two possibilities, Plurality voting is just plain wrong. Plurality voting is what many of us (particularly here in the U.S.) think of as “normal” voting: everyone votes for their favorite and whichever alternative gets the most votes wins. We use this system a lot, and we should essentially never use it. The difficulties it causes with vote-splitting and manipulation are ridiculous. It is plurality voting that causes Republicans to support a left-wing candidate in the U.S. Presidential election (and vice versa for Democrats and a right-wing third party): if the third candidate takes votes away from the Democrat then the Republican has a better chance of winning.I feel strongly enough about this that I cannot be on a committee that uses Plurality voting: I will simply argue about the voting system until it changes (or until they start “forgetting” to tell me about meetings, which is kinda a win-win).
  2. I can live with practically any other voting system. There are quasi-religious arguments about voting systems with zealots claiming one is far superior than all the others. Nothing has convinced me: the cases where these voting systems differ are exactly the cases where it is unclear to me who should be the winner. So whether it is approval voting (like INFORMS uses), or some sort of point system (“Everyone gets six votes to divide among the candidates”), or multi-round systems (“Divide three votes, then we’ll drop the low vote getter and revote”) or whatever, most of it works for me.
  3. The person setting the agenda can have a huge amount of power. While I may be happy with lots of systems, I am hyper-aware of attempts to manipulate the process. Once you give people the chance to think about things, then the agenda-setter (or voting-rule setter) can have an undue amount of power. Of course, if I am the agenda-setter, then knowing lots of voting rules can be quite helpful. Even without knowing committee preferences, it is handy to know that the following rule can help A win in an election with A, B, and C.

    Let’s do B against C then the winner against A.

    That is pretty obviously to A’s advantage (with voters voting their true preferences, A will win unless one of the others is a Condorcet winner — a candidate who could beat every other candidate in a two-person election). Less obvious is

    Let’s do A against B, then A against C, then the winners against each other

    This seems to favor A, but it does not. The only way A can win this election (with truthful voters) is for A to beat both B and C (hence be the Condorcet winner).

    In fact, my research shows that you can arrange for A to win in a four-candidate election no matter what the preferences are provided A is not the Condorcet loser (loses to every other candidate in a pairwise election) and no other candidate is a Condorcet winner. Unfortunately, no committee is willing to sit still for the 12 votes required, beginning

    We’ll go with A against B, then A against C, then take the winners against each other, and take the winner of that against D, then…

    This leads to my favorite voting tree, as in the diagram.

  4. When there is block voting, power is only weakly related to the size of the block. I have in mind systems where different “voters” have different numbers of votes. So, in a parliamentary system with party-line voting, if there are 40 representatives for party A, then party A gets 40 votes. It might seem that if the overall support is 40% for party A, 40% for party B, and 20% for party C, then it would only be fair to give parliamentary seats in that proportion. Unfortunately, if bills need 50% of the vote to pass, “proportional representation” gives undue power to party C. In fact, in this case C has as much power as A or B: two of the three parties are needed to pass any bill. Conversely, if the support is 51%, 48%, 1%, and a 50% rule is used to pass, then the first party has all the power.

    This simple observation has helped me understand the various issues with the recent U.S. Senate vis-a-vis the filibuster rules (which essentially required 60% of the votes to move anything of substance forward): the Senate vacillated between having the Democrats having all the power (51 votes to pass a bill) and having Democrats and Republicans having the same power (60 votes to end a filibuster). With no solution representing reality (either 58% of the Senate seats for the Democrats or perhaps a lower number representing nation-wide party support), the system cannot equate power with support.

    This is seen even more starkly in the election of a single individual like the U.S. President. George Bush in 2004 claimed a “mandate” after winning 51% of the popular vote. While 51% might not seem like a mandate, it is difficult how else to map 51% to a single person.

    Understanding this power relationship makes U.S. Electoral College analysis endlessly fascinating, without adding much insight into whether the Electoral College is a good idea or not.

  5. The push towards and away from the median voter explains a lot about party politics. One fundamental model in economics is the Hotelling Model.  Traditionally this model is explained in terms of ice cream vendors along a stretch of beach.  If there is one vendor, he can set up anywhere on the beach:  he has a monopoly, so no matter where the beach-goers are, they will go to the vendor.  But suppose there are more than one vendor and beach-goers go to the closest vendor. If there are two vendors, the only stable place for them to be (assuming some continuity in the placement of beach-goers) is to have both at the median point, right next to each other!  This seems counter-intuitive:  why aren’t they, say, 1/3 and 2/3 along the beach (for the case of uniformly distributed beach-goers)?  In that case, each vendor gets 1/2 of the customers, but the vendor at 1/3 would say “If I move to 1/2 then I’ll get 5/12 of the customers, which is more than my current 1/3”.  Of course, the vendor at 2/3 also has an incentive to move to the middle.  So they will happily set up next to each other, to the detriment of the beach-goers who must travel farther on average to satisfy their needs.

    How is this related to politics? I believe it gives the fundamental pressures on parties in two-party systems. In the U.S., both the Democrats and Republicans are pressed towards the middle in their efforts to get to the median voter. But the most interesting aspects are the ways in which the political system does not meet the modeling assumptions of the Hotelling model. Here are a couple:

    • The Hotelling Model assumes customers will purchase no matter what distance they need travel to the server. In a political model, voters sufficiently far away from all candidates may simply choose not to participate. While non-participation is often seen as abdicating a role, that need not be the case. Take the case of the “Tea Party Movement”. There are many interpretations of their role in politics, but one threat to the Republicans is a willingness to of the Tea Partiers to simply not participate. This has the effect, in a simplistic left-right spectrum model, to move the median voter to the left. If the Republicans want to move to the resulting median, they would have to hop over the Democrats, something that is simply infeasible (the effort to convince the left wing will take generations to believe the Republicans are really their party). So the threat of non-participation is a strong one, and can only be counteracted by the Republicans by having policies sufficiently appealing to the Tea Partiers to keep them participating. Of course, this rightward movement opens the opportunity for the Democrats to appeal to the crowd in the resulting gap between Democrats and Republicans, though the Democrats undoubtedly face non-participation threats at their own extremes.
    • Another sign of the pressures towards and away from the median occur in the primary/general election form of U.S. politics. During the primaries, a candidate (either local or national) needs to appeal to voters in their party (in most cases). This leads to movement towards the median of a party, particularly if there are only two candidates. Once the candidate has been chosen by the party, though, the candidate is now facing median pressure from the general population. this should result into a movement towards the center, which certainly seems to be the case. Party activists try to stop this move towards the center by forcing pledges or other commitments on candidates, which keep them more towards the median of their own party, perhaps at the expense of general election success.

    The Hotelling Model in politics is a wonderful model: it is wrong but useful. By understanding how the model doesn’t work, we can get insight into how politics does work.

It would be easy to be disillusioned about voting and politics based on theory (and practice, some days). No voting system is fair or nonmanipulable; pressures on candidates force them to espouse views that are not their own; consistency is obviously a foible of a weak mind.

Instead, my better understanding of voting and elections through operations research leaves me energized about voting. However imperfect it is, the system does not need to be mysterious. And a better understanding can lead to better systems.

This topic has been on my list of “to-do”s for a while. I am glad that the Second INFORMS Blog Challenge has gotten me to finally write it!

First INFORMS Blog Challenge

INFORMS has announced the results of the first Blog Challenge and it is a great success.  Fourteen bloggers had a post on the subject “OR and the Holidays” (including me!).    January’s Challenge moves into current events with the topic “OR and Politics”.  If you post on that subject, be sure to email graphics@mail.informs.org with the pointer.

Are You Ready to Lead INFORMS?

INFORMS is looking for a new Executive Director. This is a full-time staff position, unlike the volunteer elected positions like President and the various Vice-Presidencies. This position is one of the most important in our field, and certainly the most important job that does not require a PhD in operations research (though such a degree would be valuable!). It is only through the efforts of the staff that the main activities of INFORMS gets done. Even activities that are primarily volunteer-driven (like local chapter meetings, for instance) are aided by support staff at the INFORMS office. Without a good leader, INFORMS will be much less capable of getting things done, to the detriment of our field.

Here is the full announcement from the INFORMS mailing list:

INFORMS has retained JDG Associates, a firm with expertise in association executive recruitment, to conduct the search for a new INFORMS Executive Director. We welcome any suggestions from the membership of possible candidates for the position. The ideal candidate will be a strong leader, experienced in strategic planning and able to strengthen existing key programs (publications and meetings), in addition to introducing new service offerings in the field of analytics. Previous association management experience is preferred. Experience or knowledge of operations research, management science and/or business analytics is a plus.

Please contact Paul Belford, Principal, JDG Associates, Ltd, 1700 Research Blvd, #103, Rockville, MD 20850; 301-340-2210; belford@jdgsearch.com

If you know someone, be sure to let the search firm know about them! Here is some more information about the position from JDG.

The Great Operations Research Blog Challenge

At the recent INFORMS conference, a group of bloggORs (get it?) got together to discuss what sort of common activities we could do.  While we all read each others work, and periodically repost each others work, for the most part we work alone.  That is generally a good idea:  we each have a style to our blog (I wouldn’t dare to talk about vampires, for instance, leaving that to the expert).  But we are a community.  I point to all the OR blogs I can find and provide a feed of the latest posts in my sidebar;  we sit on panels together; there is the odd off-blog conversation about blogging and OR issues.  How can we strengthen that community?

Inspired by the Carnival of Mathematics , the group decided it would be fun to have a monthly theme on which we could all write.  All the resulting entries could then be collected at the end of the month.  The INFORMS Blog was elected to be the coordinator of this activity, and has just announced the first monthly Blog Challenge:

Topic for December 2010: O.R. and the Holidays

Open to all bloggers! All you have to do is a write a post on your site on that topic then send the pointer to graphics@mail.informs.org.  Given there are a couple dozen blogs in my “OR Blog Roll”, it would be great to get a big response to this. No prizes, but, as the announcement says:

What’s in it for the bloggers? Widespread fame by being listed on the INFORMS home page. Or at least a bump in readers and an increase in standing and influence in the operations research blogosphere.

Entry on “INFORMS TweetUp”

I posted on the INFORMS blog about the “INFORMS TweetUP”:

There are lots of ways to get out information on the INFORMS conference.  This blog is one of them, and it has been great to see the variety of views of the conference.  Of course, with more than 4300 participants, there will be lots of variety in how the conference is seen.

For an even more immediate view of responses to the conference, be sure to track the “#informs2010″ tag on Twitter.  A bunch of us who tweet and use that tag had an impromptu get together this afternoon.  Look for more tweets during the armadillo racing tonight!

Here are the twitter ids (I think! Corrections welcome): @mlesz1 (@informs2010), @dianam, @wjcook, @johnangelis, @miketrick, @polybot, @SDamask

INFORMS Blog entry on “Overbooking, Revenue Management, and Data Mining”

I have an entry over on the INFORMS blog regarding overbooking of hotels.  Here it is, though I recommend following the INFORMS blog for the next few days:

Fellow blogger Guillaume Roels wrote that the hotel he reserved overbooked, so he has been exiled to a remote location and he bemoaned the lack of customer service in this transaction.  Something similar was obviously going on in my hotel, the Hilton (the main hotel for the conference).  Throughout the checkin yesterday, the desk clerks were looking for volunteers to be exiled, offering various incentives (”Free transportation! A drink vouncher! Big, big discounts, just for you!”) for people to move.  They weren’t getting any takers while I was there, so I fear the late check-ins were similarly sent off to the boondocks.

I bet the hotels got into a mess because they misestimated the number of people who showed up (or overestimated the “melt”: people who canceled in the final week or two).  If they simply took an average “no show” or “cancel in the last week” rate, I bet conference participants do so at a much lower rate.  After all, the vast majority of us have preregistered for the conference, so late cancellation means forfeiting some or all of the conference registration fee.  We have great incentives to figure out early whether we are going to be here or not.  And, perhaps people in OR or other analytic fields tend to not cancel or cancel earlier due to the organized, steel-trap-like minds we all have!  We know what we are doing, so we don’t cancel in the last week.

Of course, whether or not that is true is an empirical question, and one that can be best answered by data mining methods.  Over the course of drinks last night, a senior researcher for a large business analytics firm pointed out the disconnect we have in our field between data mining and optimization.  Often (though not always), these are seem as two phases of the “operations research process”.  Instead, there is a need for much better integration between these approaches.  Data mining should be constantly in use predicting cancellations and melt, driving the revenue management optimization approaches.

For those who were bumped by the hotels last night, you have my sympathies.  Perhaps during your rides into the conference, you can plan how to integrate data mining and revenue management better in order to let hotels avoid these issues in the future.