The word “optimization” means a lot of things to different people, and I think it is a word that is a tad overused. We hear it in meetings, in product descriptions, and we even hear it on television. Most people use it as another term for “to improve.” Mathematicians view the word completely differently — to them it has a very specific meaning. To mathematicians and engineers, optimization means to achieve the mathematically provable best solution that is possible. And that is an important distinction, because “provable” is a very high bar. Mathematicians and operations researchers go to great lengths to prove that there is no other better solution to their optimal solutions.

Sixty years ago or so, a few mathematicians began developing algorithms that would solve optimization problems in a provably optimal way. The first widely known algorithm was called the Simplex Method and it solved a set of specific problems, called linear programs, optimally. This algorithm, developed by a gentleman named George Dantzig, has saved companies untold amounts of money by allowing them to develop optimal plans or schedules or processes and, hence, to operate more efficiently — provably optimally efficient.

There are all sorts of ways that mathematical optimization touches our lives today. Our GPS’s run on optimization. Our water resources are managed with optimization algorithms. Products coming to market are produced and delivered in cost-effective ways, using mathematical optimization.

In the contact center industry, mixed integer programs, a form of linear programs, have been used for many years to produce mathematically optimal agent schedules. I do not believe it is an exaggeration to say that George Dantzig and his Simplex Method has saved the contact center industry billions of dollars through the use of truly optimal scheduling algorithms. More on this in a bit.

When I was a young engineer, I developed a linear programming-based system to help schedule an airline’s ground personnel. It was a very important project at an important time; the airline was losing money hand over fist, and was well known to be operating in an extremely inefficient manner. The prior scheduling process was very low-tech — it involved an analyst who developed a scheduling spreadsheet (in Lotus 123!) and used the algorithm of “guess and test.”

Scheduling falls into the category of problems that are very difficult for human beings to answer well, because it is simply too complex a puzzle to solve efficiently without the use of algorithms. But thanks to Dr. Dantzig I had the tool, the Simplex Method, to solve this scheduling problem. The results from the algorithm were terrific — it scheduled airport agents to cover the work as near to exactly as possible, with as few extra agents as possible. Using this model saved the airline many millions of dollars, it provided consistent service, and it helped to bring the airline back toward profitability.