"Estimating Data for Multi-Criteria Decision
Making Problems: Optimization Techniques"
by Qing Chen, and Evangelos Triantaphyllou
Encyclopedia of Optimization,
(P.M. Pardalos and C. Floudas, Editors), Kluwer Academic Publishers,
Boston, MA, U.S.A., Vol. 2, pp. 27-36, (2001).
Abstract:
One of the most crucial steps in many multi-criteria decision making methods (MCDM) is the
accurate estimation of the pertinent data [18]. Very often these data cannot be known
in terms of absolute values. For instance, what is the worth of the i-th alternative
in terms of a political impact criterion? Although information about questions like the
previous one is vital in making the correct decision, it is very difficult, if not impossible,
to quantify it correctly. Therefore, many decision making methods attempt to determine
the relative importance, or weight, of the alternatives in terms of each criterion
involved in a given decision making problem.
Keywords and Phrases:
Pairwise comparisons, data elicitation, multi-criteria decision
making (MCDM), scale, Analytic Hierarchy Process (AHP),
consistent judgment matrix, eigenvalues, eigenvectors, least
squares problem, incomplete judgments.
Index:
Multi-criteria decision-making (MCDM), pairwise comparisons, data
elicitation, scales, linguistic choices, linear scales,
exponential scales, Analytic Hierarchy Process (AHP),
alternatives, criteria, relative priorities, pairwise judgment,
consistent matrix, eigenvalue, eigenvector, perfectly consistent
case, total consistency, consistency ratio (CR), consistency
index (CI), least squares, minimization of regret, minimization
of losses, error minimization, sum of squares, power method,
missing comparisons, incomplete pairwise comparison matrix,
connecting paths, missing comparisons, elementary connecting
path, geometric means method, revised mean method, Tchebyshev
norm.
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