Life is about decisions. Decisions, no matter if made by a group or an indi vidual, involve several conflicting objectives. The observation that real world problems have to be solved optimally according to criteria, which prohibit an qidealq solution - optimal for each decision-maker under each of the criteria considered - has led to the development of multicriteria optimization. From its first roots, which where laid by Pareto at the end of the 19th century the discipline has prospered and grown, especially during the last three decades. Today, many decision support systems incorporate methods to deal with conflicting objectives. The foundation for such systems is a mathematical theory of optimization under multiple objectives. Fully aware of the fact that there have been excellent textbooks on the topic before, I do not claim that this is better text, but it has a has a consid erably different focus. Some of the available books develop the mathematical background in great depth, such as [SNT85, GN90, Jah86). Others focus on a specific structure of the problems covered as [Zel74, Ste85, Mie99) or on methodology [Yu85, CH83a, HM79). Finally there is the area of multicriteria decision aiding [Roy96, Vin92, KR93), the main goal of which is to help deci sion makers find the final solution (among many qoptimalq ones) eventually to be implemented.An optimization problem is to choose among a set of aalternativesa an aoptimal onea, where optimality refers to certain criteria, according to which the quality of the alternatives is measured. ... To decide which new car to buy we consider a VW Golf, an Opel Astra, a Ford Mondeo and a Toyota Avensis. The decision will be made according to price, engine efficiency (i.e. petrol consumption), and power.
|Publisher||:||Springer Science & Business Media - 2013-11-11|