SolveIT: Fancy Math
June 5, 2008
Several years ago I found myself in a meeting. I was paid to attend a session in North Carolina; otherwise, I wouldn’t go to Charlotte. The city is too sophisticated for this Kentuckian.
In the meeting, a soft-spoken mathematician, his son, a couple of cousins, and maybe an uncle explained sparse sets, assigning probabilities to boundaries, and ant algorithms. As I struggled to dredge definitions about these concepts from my admittedly poor memory, the soft-spoken mathematician asked me a word problem. A waiter had 12 customers and ended up with an extra dollar. Why? I just sat there and looked my normal stupid self.
Later, he explained that his inspiration was a mathematician named Stanis?aw Le?niewski. Okay, early 20th century wizard. That was the end of my knowledge. Puzzles are the key to learning math he told me. In his spare time, this fellow has set up a Web site to make this concept more widely known. You can see it here.
I had no clue who these fellows were, but I was getting paid to listen so I listened.
A Super Guru: Who Says He’s Just a Regular Guy
The super guru is a fellow named Zbigniew Michalewicz, a highly regarded mathematician everywhere except in Harrod’s Creek. The relatives were also mathematicians. The crowd could finish one another’s sentences and equations. Math, it turns out, is something that runs in the Michalewicz family and has for decades.
Dr. Michalewicz is an expert in generic algorithms and data structures. When added together, the mathematical recipe yield evolution programs. You can read more about this approach to some tough data problems in Genetic Algorithms + Data Structures = Evolution Programs, published by Springer-Verlag ISBN: 3-540-60676-9. No, your local book store won’t stock it. Amazon does.
The group sold its US enterprise and Dr. Michalewicz and a family member or two moved to Australia.
After losing track of these fellows, I learned that Dr. Michalewicz, his son, and a handful of mathematical gurus set up shop as SolveIT Software. Click here to navigate to the company’s Web site.
The new company uses new math to solve old problems. The company is in the business of delivering solutions that deliver “adaptive business intelligence”. The company’s range of technology is remarkable and it may be meaningless to you unless you took a couple of advanced math classes; for example:
- Agent-based systems
- Ant systems (my favorite)
- Evolutionary strategies
- Evolutionary programming
- Fuzzy systems
- Genetic algorithms
- Neural networks
- Rough sets (great stuff!)
- Swarm intelligence
- Simulated annealing (does with math to data what oil quenching does to low-grade steel)
- Tabu search (I have no clue what this numerical method yields).
You can figure out most of these notions by dipping into Peter Norvig’s Artificial Intelligence or E. J Borowski’s and J. M. Borwein’s Web-Linked Dictionary Mathematics. (Note: there is a subtle difference between the Norvig approach and the Michalewicz method. Google uses humans. Humans play an optional role in the Michalewicz recipes. No big deal, but you can explore the differences yourself by reading each guru’s text book.)
A Case Example
Equations are not likely to raise my Google ranking. Let me describe an outcome of Dr. Michalewicz’s skills.
Here’s the set up. You are Ford, Honda, or Toyota. Each week you get a couple of thousand lease cars back. You want to sell the cars quickly. You want to minimize how much you have to spend to truck these white elephants to a location where a particular model will sell. Pink convertibles don’t fly in Nome, Alaska, but are hot items in Scottsdale, Arizona. Your resale team would rather go to a bowling convention that work Excel models.
You want to maximize return, minimize expenses, and get the decisions out of your resale team’s “instinct” and into something fungible like a SolveIT solution.
SolveIT’s analysts beaver their way through the data, the work flow, and the exogenous factors that you and your resale team did not consider. The company builds from its mathematical Lego blocks, a computerized system that prints out a map and report telling your sales team where to ship which car.
You use the SolveIT system for a couple of months, and you notice that your expenses go down and your net goes up. SolveIT removed the guess work and let the “fancy math” do the heavy lifting. When I spoke with the company several years ago, one beta client was generating cash positives in six figures within six weeks.
Like most sophisticated companies run by serious math geeks, there’s not much information available on the company’s Web site. I did dig through my files, and I found an example of the company’s outputs. Keep in mind that this diagram is probably out of date, but it will give you a flavor of what the SolveIT operation does.
The system “shows” the resale team where certain cars will sell. Then the system prints out a report that says, “Send the pink convertible to Chicago and the truck to Paducah.” The math does the heavy lifting. The resale team looks at simple diagrams. The math remains safely hidden away.
Observations
SolveIT is one of a handful of companies pushing the envelope in analytics. If you want to tap into some serious math, contact this company. I have one tip. Don’t ask, “How does this work?” The explanation requires a solid foundation if traditional mathematics and post-doctoral work in set theory. How complicated is the math. I found in my files one example which I had to scan and convert to an image. I kept it as a reminder of how little I know about the next big things in mathematics; for example, in my notes I had this pair of statements:
If these statements speak to you, then you can dig more deeply into the SolveIT systems and methods.
Based on my personal experience with Dr. Michalewicz, he’s a capable mathematical thinker. For more about his company’s approach to problem solving, you will find useful How to Solve It: Modern Heuristics, also by Springer Verlag. You can get a copy here.
Stephen Arnold, June 6, 2008
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