Category Archives: Uncategorized

dynamic programming day

Eric Dubois, one of my PhD students, interned at the RAND Corporation this summer. He gave a presentation about his internship to my lab.

I learned that dynamic programming is still of great importance at RAND. Richard Bellman introduced dynamic programming in 1953 while working at RAND. He spent most of his career at RAND, and his many contributions to dynamic programming are still cherished. You can download his 1954 RAND Report “The Theory of Dynamic Programming.” Every summer, RAND employees celebrate dynamic programming’s anniversary with cake.

I would love to celebrate dynamic programming with cake and with the cake eating problem (optimal depletion of an uncertain stock).

Note: the cake eating problem can be solved with dynamic programming.

The RAND Corporation began to provide analysis for the Air Force after World War II. Soon thereafter RAND branched into nuclear deterrence. A (fake) RAND analysis on nuclear deterrence is mentioned in Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb.
The Dr. Strangelove character is based on RAND scientist Herman Kahn.

what Punk Rock #ORMS is reading and listening to

What I’ve been reading

What I’ve been listening to

how to write a scientific paper: advice told through a series of tweets

It’s almost the end of the summer, which marks the end of writing season in academia. Here is some advice for writing a scientific paper, as told through a series of tweets. What would you add?

What Punk Rock #ORMS is reading: The #Olympics edition

  1. How to resolve ties in Olympic events: and old Punk Rock OR post
  2. Why are their so many ties in swimming when the timing equipment can measure to the millionth of a second?
  3. Sprinters should start fast, everyone else should finish fast. An article on pacing during running events at the Olympics (with data and charts!)
  4. Katie Ledecky is so dominant she is like the Secretariat of swimming. An update to this article is here.
  5. Why nearly every sport except long distance running is fundamentally absurd
  6. Want to see a faster Olympic marathon? Move it to the Winter Olympics
  7. What ever happened to the long jump? The world record set 25 years ago “has edged against the limit of human potential, leading fewer athletes to take interest in ever challenging it—a negative feedback loop of fewer elite athletes competing in long jump and less television time being dedicated to it”
  8. “Heptathletes win points according to obscure, nonlinear formulae, inspired by a Viennese mathematician, Karl Ulbrich”
  9. The story behind the perfect photo of Olympic pain: an article about Mary Decker’s fall during the 3000 meter run during the 1984 Olympics
  10. New York Times interactive on Olympic athletes who were denies their medals because others were doping

Heilmeier’s catechism: not all research that is “interesting” is also “important”

Legendary engineer George Heilmeier came up with a set of questions to help program managers evaluate proposals while he was director of DARPA (1975-1977):

  1. What are you trying to do? Articulate your objectives using absolutely no jargon.
  2. How is it done today, and what are the limits of current practice?
  3. What’s new in your approach and why do you think it will be successful?
  4. Who cares? If you’re successful, what difference will it make?
  5. What are the risks ?
  6. How much will it cost?
  7. How long will it take?
  8. What are the midterm and final “exams” to check for success?

This list is still used today. I most often see this list in presentations by National Science Foundation program officers who are interested in helping researchers write competitive proposals.

I love this list.

This list has stood the test of time because it’s a great list. I find questions #2 and #4 to be particularly helpful. I do so because my first inclination is to talk about why my research is interesting and exciting to me. I wouldn’t start research in a new area unless the topic were interesting and unless my skill set were brought something to the table. Being excited about my interesting research is not sufficient for giving a good answer to #2 and #4. Not all research that is “interesting” is also “important.”

When I write with my students, we talk about how we need to answer #1-#4 in the paper, although we answer slightly different versions of the questions since the research has already been successful if we are publishing the results.

I once summarized how to answer an abridged version of the Heilmeier questions in the first couple minutes of a thesis defense. When a student did so, it made for a memorable defense (in a good way!) and I tweeted about it.

Related posts:

13 reasons why Hillary Clinton will (probably) win the Presidential Election

There are 96 days until Election Day, but I’m already pretty sure Hillary Clinton will win the election. The Keys to the White House by Allan Lichtman and Vladimir Keilis-Borok is a simple mathematical model that predicts who win a Presidential election. This model predicts who will win months or even years before an election. You can read the writeup in OR/MS Today here. Let’s look at why Hillary will likely win in 96 days.

The model works by considering 13 factors that are equally weighted in the model. The reference point is the person running in the same party as the incumbent President, which is Hillary Clinton in 2016.

1. Party Mandate: After the midterm elections, the incumbent party holds more seats in the U.S. House of Representatives than after the previous midterm elections.
FALSE: 193 Democrats in 112th Congress but 188 in 114th Congress

2. Contest: There is no serious contest for the incumbent party nomination.

3. Incumbency: The incumbent party candidate is the sitting president.

4. Third party: There is no significant third party or independent campaign.
TRUE (so far!)

5. Short term economy: The economy is not in recession during the election campaign.

6. Long term economy: Real per capita economic growth during the term equals or exceeds mean growth during the previous two terms.
TRUE: 1.6% vs. 1.5% and 1.4% Source:

7. Policy change: The incumbent administration effects major changes in national policy.

8. Social unrest: There is no sustained social unrest during the term.

9. Scandal: The incumbent administration is untainted by major scandal.

10. Foreign/military failure: The incumbent administration suffers no major failure in foreign or military affairs.

11. Foreign/military success: The incumbent administration achieves a major success in foreign or military affairs.

12. Incumbent charisma: The incumbent party candidate is charismatic or a national hero.

13. Challenger charisma: The challenging party candidate is not charismatic or a national hero.

There are five “Falses.” When five or fewer statements are false, the incumbent party wins. When six or more are false, the challenging party wins. It looks like barring a surge ahead for third party candidate to something like 1992 Ross Perot levels (see #4), five or fewer statements will continue to be false. I’m not sure if the model is flexible to account for a divisive figure like Donald Trump, but we will find out soon.

What is interesting is that this model requires no polling information, which is a major input requirement to most other models (like the one at FiveThirtyEight). It instead looks at underlying causes for support for the political parties based on how satisfied we are with various things that have happened, hence the “keys” about social unrest, war, major policy change, major scandal, and the economy. I blogged before about the importance of the economy in making Presidential election forecasts (“It’s the economy stupid“).

Do you think traditional ways to forecast the election will “work” this year?


Open problems & unsolved mysteries in operations research

When I attend conference talks, I sometimes hear a speaker mention how a problem is “open.” Sometimes the open problem is of interest only to the speaker and sometimes the open problem is of interest to the whole community. I am blogging about the open problems in computational operations research that have broad appeal.

A list of open problems in computer science include some familiar open problems in operations research, including:

  • Does P-NP?
  • Does linear programming admit a strongly polynomial-time algorithm?

There are open problems in operations research too.

Saul I. Gass and Arjang A. Assad proposed a list of great unsolved problems in operations research (GUPOR – not my acronym!) in a 2007 OR/MS today by soliciting experts.

  1. Need and Potential for Real-Time Mixed-Integer Programming by George L. Nemhauser Engineering grand challenges
  2. Increase in Flight Delays Calls for Better Air Traffic Management by Michael O. Ball
  3. Responsibility of O.R. for Disaster Management by Martin Starr

Several sessions at the 2006 INFORMS Annual Meeting were devoted to these problems as well as other problems such as healthcare delivery.

More recently, Mikael Rönnqvist, Sophie D’Amours, Andres Weintraub, Alejandro Jofre, Eldon Gunn, Robert G. Haight, David Martell, Alan T. Murray, and Carlos Romero wrote OR challenges in forestry: 33 open problems. I won’t list all of the open problems, but will say that many are of general interest and involve transportation problems such as the vehicle routing problem.  You can read the full paper in Annals of Operations Research here.

And finally, two years ago I blogged about engineering grand challenges that operations research can help solve from an NSF report. Challenges areas from the report include:

  1. OR: A General-Purpose Theory of Analytics
    “The time has come to engage both domain experts as well as OR experts, so that policies/decisions become an integral part of analysis, not an afterthought.”
  2. OR for sustainability
    “The Earth is a planet of finite resources, and its growing population currently consumes them at a rate that cannot be sustained. Utilizing resources (like fusion, wind, and solar power), preserving the integrity of our environment, and providing access to potable water are the first few steps to securing an environmentally sound and energy-efficient future for all of mankind.”
  3. OR for security
    “As our interconnected systems grow in complexity, having a trusted operational model is even more essential for assessing system vulnerabilities and, in turn, addressing the challenge of how to secure that system.”
  4. OR for human health.
    Also see my last blog post on healthcare challenges – I’m glad the White House and the OR community agree with this one!
    “One of the most significant problems facing the health care system is keeping costs under control while providing high levels of service. Doing so requires a careful analysis of costs and benefits, but as Kaplan and Porter (2011) argue, “The biggest problem with health care is that we’re measuring the wrong things the wrong way.” “
  5. OR for Joy of Living
    “For example, reducing traffic congestion in urban areas, improving response times of first-responders, designing smart, energy efficient homes, and others raise many novel OR questions. One such example is an application related to predicting movie recommendations associated with the so-called “Netflix Prize” problem. Other “joys of life,” such as sports, have also seen many applications of analytics; in addition to the well publicized baseball movie “Moneyball,” there is Major League Baseball scheduling which is done routinely using OR models. In this sense, OR casts such a wide net in the “Joy of Living” area, that the following subsections (pertaining only to the NAE Grand Challenges) explicitly discuss only a small subset of applications for “Joy of Living.” “

Which problems do you think should be on the list of open problems in operations research?