al attributes and Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website. Capacity and Quality Tradeoffs Capacity and Quality Principles we have to make a tradeoff decision, it is easiest if we can define one driver. We then do the best we can on that one attribute given other constraints and working with de- grees of freedom. For example, if we are asked to make something as good as it can be for under $500 and it does not matter if we have it ready tomorrow or in 6 months, then quality is the driver, cost is the constraint, and time has a large degree of freedom. Of course, practical situations are rarely this clear-cut. A customer may give me the preceding requirements and then, when I call him or her back with a solution that costs $502, be perfectly happy. On the other hand, another customer may genuinely have an absolute limit, perhaps due to regulatory requirements on expenses. In communicating with our customers, it is very important to be clear about each requirement, its con- straints, and its degree of freedom. We are often given problems with multiple drivers. We might receive these instruc- tions: Make it good, but keep costs down. In this case, it is best to propose scenarios with costs to the customer, trying to turn one of the drivers (cost) into a constraint. 22.2 Quantitative Optimization Optimization is the science of making things as good as they can be. We can break the process up into three component parts: Figuring out what is good. Figuring out what can be. Figuring out the best solution. Actually, there are many sciences and technologies that fit under the umbrella of opti- mization, and those who practice the art of optimization usually have a subspecialty. What area of optimization is relevant for a particular problem depends on the particu- lar circumstances. What is good can be a single variable or a single expression, as 