Someone I knew in college heard Smith, called me, and I flew from Maryland to FedEx and was hired to solve the problem.
Back in Maryland, we had some meetings, including one in a conference room at the Georgetown library, with various approaches to the problem, none good. There was some politics involved.
Really, the project was just mine, so I thought of a first-cut approach and attacked the problem; I was still teaching computer science at Georgetown. In six weeks I had a program, turned in the grades for the courses I was teaching at Georgetown, drove to Memphis, rented a room, tweaked the program a little, and declared it done.
Soon too many on the Board were saying that there could be no good solution to the scheduling problem. So, one evening a senior VP and I used my program to schedule the whole planned fleet into all the planned cities, printed out the schedule, and submitted it.
At a senior staff meeting, Smith's reaction was, to paraphrase, "An amazing document; solved the most important problem facing FedEx".
Our two Board representatives from Board member General Dynamics went over the schedule carefully and announced, "It's a little tight in a few places but it's flyable.". The Board was happy and a big chunk of equity funding was enabled.
So, the software solved the practical problem at the time and in time. But the software was not a grand solution to everything in fleet scheduling.
The hard parts were, (a) the politics, (b) designing a program that would be powerful enough to solve the practical problem at the time but easy enough to write to solve the problem in time.
Later I attacked the problem via 0-1 integer linear programming set covering; but the politics got much worse; the promised stock was very late and still just a handshake deal with Smith with only my offer letter on paper; my wife was still in Maryland; and I wanted either the stock or a Ph.D. Smith's last promise of stock was $500,000 worth that might be worth 1000 times that now, but it was just a handshake deal. So, left for a Ph.D.
(2) Nuclear War at Sea. To support my wife and I to the end of our Ph.D. degrees, I took a part time job in military systems analysis. At one point the US Navy wanted to know how long the US SSBN fleet would last under a special scenario of global nuclear war but limited to sea. They wanted their answer in two weeks.
There was an old paper of B. Koopman that argued that 'encounters' at sea between Red and Blue weapons systems would form a Poisson point process. I added on a little and got a continuous time, finite state space Markov process. There is a closed form solution as a matrix exponential, but due to a combinatorial explosion the state space was far too large for doing anything numerical with that solution.
But it was easy enough to generate sample paths, so I wrote software to generate and average, say, 500 sample paths. The work passed a technical review by a famous mathematician, and the Navy got their results on time. The next day we took a vacation in Shenandoah, and my wife got a vacation she wanted on time, too!
(3) Winning a Contract Competition. There in Maryland I was in a software house working for a US Navy lab. We were in a competition for a software development process. Part of the work was to measure the power spectrum of ocean wave noise. I got smart on power spectral estimation and wrote some illustrative software of passing white noise through a filter with a specific transfer function and accumulating the empirical power spectrum of the output of the filter. The software showed what the math claimed: At the low frequencies the project wanted, an accurate power spectrum needed a surprisingly long interval of data. The software showed that with short intervals of data, the estimated power spectrum could have big peaks that, really, were just sampling noise that would go away as the length of data increased.
So I called one of the customer's engineers and showed them the news. As a result, our software house won the competition.
(4) Anomaly Detection. I was working on applying artificial intelligence to the monitoring and management of server farms and their networks. One of the main techniques was 'thresholds', and I wanted something better.
So, I put my feet up, popped open a cold can of Diet Pepsi, reviewed some of my best graduate school material including some of ergodic theory, had some ideas, wrote out some theorems and proofs, and wrote some corresponding software.
So, suppose are given a system to monitor. Suppose 100 times a second we get data on each of 15 variables. Collect such data as 'history' data ('learning data') for, say, three months (assume a stable server farm or network). Study this data, let it 'age', and be fairly sure that the system being monitored was 'healthy' during that time. Then in real time 100 times a second, report if the system is 'sick' or 'healthy'. Have false alarm rate known in advance and adjustable over a wide range. So, have a statistical hypothesis test that is both multi-dimensional and distribution-free, one of the first such. Although there is not enough data on actual anomalies to apply the Neyman-Pearson result, do process the data in a way to promise relatively high detection rate for any selected false alarm rate. Find a way to do the computations quickly and efficiently.
I did those things.
Some office politics got involved: Suddenly I was told to write a paper on my work and that the company would review the paper. If the paper was not publishable, then I would be fired. I wrote the paper; the company claimed that the paper was not publishable; and I was fired.
The guy who walked my out the door had been in management for about 20 years but was demoted out of management the next day. The main guy after my ass was two levels higher up, pissed off at me for no good reason, two weeks later was demoted one level in the organization chart, ws given a "performance plan", which he failed, and was demoted out of management.
The company wrote me a letter giving me intellectual property rights to my work. Out of the company, I submitted the paper for publication. The paper was published in a good Elsevier journal, the first journal to which the paper was submitted, without significant revision (one reviewer wanted to change how the first line of each paragraph was indented). The Editor in Chief of the journal invited me to give the paper at a conference he was running -- I declined.
(5) Internet Search, Discovery, Recommendation, Curation, Notification, and Subscription. My view is that current Internet search techniques are effective for only about 1/3rd of Internet content, searches users want to do, and results they want to find. I want the other 2/3rds.
With my feet up again and another cold can of Diet Pepsi, I had some ideas and wrote the corresponding code. Now all the code is written for a corresponding 'search engine' Web site except I have a nasty little bug having to do with class instance de/serialization. Should fix that today. Should be live in a few more months.