Algorithms in the Attic

The February 2007 issue of the Harvard Business Review has an article on “Breakthrough Ideas for 2007” (sorry, pay-only link: HBR isn’t much for making stuff available for free). One of the breakthrough ideas is given by Michael Schrage of the MIT Media Lab under the title “Algorithms in the Attic”, a play on the title Rembrandts in the Attic, a book on unlocking the value of patents (for better or worse). Schrage argues:

For a powerful perspective on future business, take a hard look at mathematics past. As computing gets ever faster and cheaper, yesterday’s abstruse equations are becoming platforms for tomorrow’s breakthroughs. Companies in several industries are now dusting off these formulas and putting them in the service of new products and processes.

Examples Schrage cites are include:

Procter & Gamble has been restructuring its supply chain with complex “expressive bidding” algorithms–based on 1950s linear-programming equations– that allow suppliers to bid online with bundled offerings of products and service levels rather than with standardized lots. Google’s search engine was possible only because the founders adapted a century-old theorem about matrices to software for ranking Web pages according to links from other sites. Networks like the Web can be expressed as matrices, and a relatively simple calculation gives a ranking of how well each site is connected to the rest of the Web. That formula for automatic ranking – which could be understood and appreciated without a PhD – is one of the most lucrative algorithms ever. The math was there for the taking.

Well-known OR researcher Dick Larson gives a nice quote:

“There are huge hidden assets in the operations-research community,” says the MIT professor Richard Larson, a pioneer in probabilistic modeling techniques. “If you gave an army of 20 grad students the mission to rake through the published literature of the past 30
years, they would find stuff that has untapped business potential worth billions
of dollars. There are many clever ideas my students worked on decades
ago that in today’s networked environment would not be an academic exercise
but a real business opportunity.”

Schrage concludes:

Whether looking for breakthroughs or just trying to improve decision making, companies will benefit from greatersophistication around even simple mathematics. A decade ago, big firmsbegan to realize that they were sitting on a treasure trove of underutilized patentsand know-how that could be commercialized for willing buyers. Those “Rembrandts in the attic,” as Kevin G.Rivette and David Kline put it in their 2000 book by that name, needed thekeen eye of an intellectual property curator to appreciate their value. Similarly, we now require quantitative entrepreneurs to seek out existing equations that meet today’s pressingbusiness needs. Technology continues to make that quest faster, easier, and cheaper.

Hmmm…. thinking over my past research doesn’t immediately lead to thoughts of business opportunity, but maybe it is time to pull out those dusty journal articles.

Math, Poker, and Perfectly Reasonable Deviations

The Math and Poker blog is one I like to go to periodically (not the least because I am part of his blogroll). The thing I like best is the use of simple probability/queueing/stochastic systems to point out the misconceptions of many gamblers. A little probability goes a long way, but it seems that many gamblers are not willing to even take the first steps in probability theory.

A recent post on that blog pointed me to Perfectly Reasonable Deviations, a blog on “random ramblings on science, technology, and other such stuff”. A recent entry there reviewed the book Convex Optimization by Boyd and Vandenberghe. An excerpt from his review:

It’s a wonderful book! A masterpiece! A joy to read! Quite likely one of the best math books I have ever read.

I agree!

Linda Green on OR in Healthcare

Linda Green of Columbia University was here (Auckland) today and gave a talk on the use of operations research in the health care industry. Most of her presentation was on simple queueing models to gain insight into capacity and scheduling for healthcare. Some of this work has recently been covered in Business Week. One simple “Queueing 101” result that people just don’t understand is that a queueing system with 85% utilization, say, is not 15% inefficient. In fact, this value is the rule of thumb many hospital administrators use to determine optimal intensive care unit (and other hospital unit) sizing. But such a utilization can lead to 10% or more of the patients arriving with no beds for them! The exact number depends on the variability and other statistical measures of the arrival and service processes but aiming for 95% utilization (as some hospitals and districts do) is foolish and dangerous: it will lead inevitably to many turned away (or choosing to leave without being seen, leading to more critical later issues). The world would be a much better place if more people understood some of the basic insights that OR provides.

NCAA Tournament and Operations Research

Final Four LogoIt is time again for the year’s best sports event: the NCAA college basketball tournament. 65 teams in a single elimination tournament held over 3 successive weekends, with the winner taking all. Picking the teams for the tournament is a favorite conversation topic over coffee and beers (not that most of us can distinguish among the 336 eligible teams!). Joel Sokol, Paul Kvam and my research and business partner George Nemhauser have a system entitled the “Linear Regression/Markov Chain” (LRMC) to rank teams. You can get the pre-tournament rankings here. These rankings suggest that the four top teams in the land are North Carolina, Kansas, UCLA, and Texas A&M. The tournament committee picked Ohio State and Florida (number 5 and 6) instead of UCLA and Texas A&M.

If you are filling out your bracket, you can use the rankings to guide you (just pick the highest ranked team at each step). As an article in the Atlanta Journal Constitution writes:

LRMC’s most striking predictions this year: Picking No. 12 seed Arkansas over No. 5 seed Southern Cal and No. 10 seed Georgia Tech to knock off No. 7 seed UNLV.

The tournament committee this year had access to LRMC:

Nemhauser, a former Tech faculty athletics representative, arranged to provide a special version of LRMC to the selection committee for the first time this year. (The committee wouldn’t look at a ratings system that considered victory margin, so it got one with that component factored out.)

“It’s certainly one tool people could look at,” committee chairman Gary Walters said, but he went on to praise the RPI and the Sagarin ratings and to call the RPI “our primary quantitative tool.”

It will be interesting to see how some of the predicted upsets work out: perhaps the tournament committee will need a new primary quantitative tool.

Optimizing Airline routes

Thanks to Chad, a Tepper MBA student, who pointed out the the Wall Street Journal of March 6, 2007 has an article on optimizing international airline routes. It turns out that countries have complicated costs for airplanes flying in their space, and rerouting the planes can lead to pretty significant savings (on the order of $1,400/flight, which multiplies out to millions per year). From the article:

The formulas countries use to compute their overflight fees vary widely. Cameroon, in Africa, bases its charges on the maximum takeoff weight, with an international flight passing over the country paying from €102 to €204 ($134 to $267). Canada assesses 3.6 Canadian cents (3 U.S. cents) times the distance flown times the weight of the aircraft, plus C$97 per flight for air-traffic control coverage over the North Atlantic. United Kingdom overfly rates are three times as high as Ireland’s rates, and German rates are at least twice as high as Canada’s, according to international aviation specialists.
The U.S. funds its air-traffic-control system differently. Instead of flyover fees, passengers pay multiple taxes to the airlines, which pass the money on to a trust fund. On domestic flights fliers pay 7.5% of the cost of the ticket, plus $3.40 for each flight segment. Passengers on international flights pay $15.10 in fees for coming into and leaving the U.S. Minimizing overflight fees must be balanced against additional fuel costs if an alternative route is less direct. The software systems track a slew of additional data: weather; airport locations and runways; weight and performance of each airplane in the carrier’s fleet; temporarily blocked airspace and the locations of fixed air routes, which change daily due to winds.
The computers churn through multiple scenarios, including minimum time, minimum fuel and minimum cost, and determine which is the best solution for the maximum payload, given up-tothe-minute wind and weather information.

It is surprising the savings possible:

British Airways in 2003 finished installing such software on its route network, replacing a homemade system that one pilot describes as “a blunt instrument.” As a result, it has drastically changed the routing of its Sao Paulo-London flight. Instead of flying in a straight line and crossing over Portugal, Spain and France before entering U.K. airspace, the airline now takes a northerly track over the Atlantic and enters the U.K. over Cornwall. Capt. Tim de la Fosse, BA’s general manager of flight technical, says the new path saves £3,000 ($5,767) per one-way flight in European overfly fees, uses one fewer ton of fuel and is 18 minutes shorter in duration.

More on Genetic Algorithms

After the last posting on David Goldberg’s lab, I note another genetic algorithms oriented blog in the Genetic Argonaut by Marcelo di Brito. The current post is on optimizing the shape of a lens. The article gives a detailed description of a nice experiment to find the best lens with a variety of genetic algorithm-based approaches. It is a good introduction to some of the newer additions to the basic genetic algorithm. I would love to see how this compares to optimization-based approaches.