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Intelligent Strategies for Meta Multiple Criteria Decision Making (Hardcover, 2001 ed.)
Loot Price: R3,090
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Intelligent Strategies for Meta Multiple Criteria Decision Making (Hardcover, 2001 ed.)
Series: International Series in Operations Research & Management Science, 33
Expected to ship within 10 - 15 working days
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Multiple criteria decision-making research has developed rapidly
and has become a main area of research for dealing with complex
decision problems which require the consideration of multiple
objectives or criteria. Over the past twenty years, numerous
multiple criterion decision methods have been developed which are
able to solve such problems. However, the selection of an
appropriate method to solve a particular decision problem is
today's problem for a decision support researcher and
decision-maker. Intelligent Strategies for Meta Multiple Criteria
Decision-Making deals centrally with the problem of the numerous
MCDM methods that can be applied to a decision problem. The book
refers to this as a meta decision problem', and it is this problem
that the book analyzes. The author provides two strategies to help
the decision-makers select and design an appropriate approach to a
complex decision problem. Either of these strategies can be
designed into a decision support system itself. One strategy is to
use machine learning to design an MCDM method. This is accomplished
by applying intelligent techniques, namely neural networks as a
structure for approximating functions and evolutionary algorithms
as universal learning methods. The other strategy is based on
solving the meta decision problem interactively by selecting or
designing a method suitable to the specific problem, for example,
the constructing of a method from building blocks. This strategy
leads to a concept of MCDM networks. Examples of this approach for
a decision support system explain the possibilities of applying the
elaborated techniques and their mutual interplay. The techniques
outlined in the book can be used by researchers, students, and
industry practitioners to better model and select appropriate
methods for solving complex, multi-objective decision problems.
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