ficult problem in human negotiation as well. Members of a team who are trying to find the best solution are likely to think that their own parts of the problem are the most important. There is another side to the optimization formulation. Decision support problems often have different formulations for the same goals and constraints. One formula- tion of a problem might take a computer thousands of times longer to solve than another formulation of the same problem. The difference in computational effort can be particularly great when an integer solution is required, one consisting of whole numbers. Sometimes we can gain computational efficiency by interchanging objectives and constraints. We may be trying to serve a given customer community at the lowest cost, but the problem may be far easier to solve with an objective of maximizing users served at a fixed cost. Solving one problem is the practical equivalent of, or at least similar to, solving the other problem, but the mathematics and computation may be dramatically different. 22.6.1 Multiple dimensions of quality is important to remember that objectives can take many forms, particularly when we are trying to improve quality. Pick any industry, any business, any technical area, there are almost certainly many aspects to consider in optimizing quality. Some prob- lems get virtually the same answer as long as all the important aspects appear in the objective function whereas others have more specific tradeoffs to be optimized. These other problems are the ones where it is most important to define the objective function and to understand its relative emphasis on the different parts of quality. 7We can set a radio to transmit at half power, but we cannot put half a base station at a specific location. 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 Q