ical models that are computationally manageable. While one can define an economic model of goodwill or customer satisfaction, getting a community of business professionals to agree on such a formulation is often a challenging problem in itself and not a mathematical one. Thus it is usually an incorrect use of jargon to refer to many of our solutions as opti- mal, and we often hear people refer to later refinement as a more optimal solution, a term that grates on the ears of a professional mathematician. In fact, we are look- ing for good answers, then better answers, but not necessarily some kind of perfect optimum. Consider a complex business system, such as a wireless telephone network, that has many component parts and many decisions to be made. In the case of a large wireless telephone network, the decisions include the placement of hundreds of base stations, the design of the backhaul network from those base stations to the mobile switching centers (MSCs), the services to be offered, and the choice of regions where advertising should be concentrated. The wireless service provider swims in a sea of decisions rang- ing from small day-to-day decisions to big long-term decisions. The ability to run the network at a profit depends on the quality of these decisions. Rather than experiment on the physical system itself, we conduct thought experi- ments, make changes in a mental picture of the system, and try to visualize the results. This mental picture is often a computer simulation or a mathematical model; it can be a profit-and-loss business case or an engineer's visualization of the entire system. It is in the realm of the thought experiment that we optimize our decisions. Some of the decisions are made among a limited set of choices. We call these choices discrete variables. They can be as simple as yes or no or as sophisticated as selecting the handoff neighbor lists of each cell sector. In any case, discrete variables have a finite number of choices. Other decisions are continuous variables and have an infin