Most papers dealing with multiple criteria problems re
gard the criteria as given and confine the analysis to a de
cision of how to achieve the solution.
When dealing with multiple criteria problems the deci
sion maker usually says he considers many criteria, although often only few of them are essential.
Many authors (see e.g. Fishburn 1964) have found that normally at most 4 to 7 criteria are important, as working
with few measures of effectiveness from the beginning increases the chance of success, there being less spread in necessary data. Other reasons for using few criteria include lower cost
- 3 4
and greater ease in estimating and using the multiple criteria model.
The problem of handling a large number of criteria has not been very much treated in multiple criteria literature.
Fishburn (1964) discusses three approaches to this problem.
The first is to select a subset (about 6 to 10) of cri
teria most important to the decision maker and to use only these in the analysis.
Another method is first to select a subset of the more important criteria and to analyse the alternatives in terms of these criteria. Then another subset of criteria is added to those initially used and the alternatives are analysed with respect to this larger subset of criteria. If the results of this second analysis agree with those of the first one, the decision mal- 3r may be satisfied with the analysis. If the out
comes of the. analysis differ, the decision maker adds a new subset of criteria and repeats the analysis.
The third approach for reducing a great number of criteria suggested by Fishburn is to select any subset of criteria and analyse the alternatives with respect to these criteria. Next a new subset of criteria is selected and processed and this process is repeated several times.
If the results of the analysis of the alternatives are
le approaches based on the representation of criteria by in
cidence matrices are used.
Another possibility how to test the mutual relation bet
ween just two criteria is the use of rank correlation methods, especially the use of the disarray coefficient.
While the use of the coefficients mentioned above requires the transformation of the criteria into binary matrix (corres
ponding to the pairwise evaluation of the alternatives) the use of disarray coefficient requires to represent the criteria as rankings of alternatives.
The analysis of more than two criteria is usually perfor
med for the following reasons:
- to reduce the number of criteria,
- to find out which criteria or which subsets of criteria in
fluence to the greatest extent the solution, - to test the set of criteria for consistency,
- to examine mutual relations between subsets of criteria.
To solve the majority of the problems mentioned the method of automatic generating of hypotheses (GUHA) can be used.
This method is applicable to all problems in which it is required to obtain unknown laws, relations or causal connec
tions. Its usefulness consists in the combirCations of the for
mal apparatus of mathematical logic, the operational capabili
ties of computers and of methodology of scientific research.
The means of mathematical logic make it possible to find a suitable class of formalized statements to which the investi
gation of the model can be confined. The means of computer other, the method of cluster analysis can be applied.
Cluster analysis is concerned with very general problem of grouping the entities of a given set into homogenous and well separated subsets, called clusters. To define a particular
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-cluster analysis problem it is necessary to precise the con
cepts of homogenity and separation. There exist different ways to construct dissimilarities from measurement of characteris
tics (i.e. from the values of criteria on the set of alterna
tives). One of the possibilities is to calculate simply the in
tercorrelations of the criteria; another possibility is to find for any pair of criteria disarray coefficients (see Kendall (1955)).
Another possibility how to use the rank correlation approach is to test the consistency of the set of criteria using Kendall's coefficient of concordance as a measure of agreement of m rankings:
W = *25 2 , 3 , m (n -n)
All the techniques mentioned above have been tested on real life multiple-criteria problems.
REFERENCES
Benayoun, R. and others: Linear Programming with Multiple Objective Functions: Step Method (STEM), Mathematical Programming 1, 1971, p.366-375.
Cerny, M . , Glückaufóvá, D., Toms.M.: Metody komplexniho vyhodnocování variant, Academia Praha, 1980.
V f f
Cerny, M. , Gliickaufova,D. : Aplikace metod vicekriteriál- ního vyhodnocování, SNTL Praha, 1982.
Fishburn, P.C.: Decision and Value Theory, Wiley, New York, 1964.
Glückaufóvá,D . : Nekteré nővé smery vyvoje metod vyhodno
cování (ELECTRA III), EMO 1, 1982.
Kendall,M.C.: Rank Correlation Methods, Charles Griffin, London, 1955.
Lagreeze,E.J .: How we can use the notion of semiorders to build outranking relation in multicriteria decision making. Metra, Vol XIII, No 1, 1974.
Luce, R.D.: Semiorders and a theory of utility discrimi
nation, Econometrica, Vol. 24, 1956.
Metoda GUHA, Skriptum vydané ke stejnojmennému kursu.
V y/ ^ ^ Q
Ceskoslovenská kybernetická spolecnost pri CSAV. Dum
^ 'J .
techniky CVTS Ceske Budejovice, 1976.
Roberts, F.S.: Homogeneous Families of Semiorders and the Theory of probabilistic Consistency, Journ. of Math.
Psych., 8, 1971.
Roy, B . : Classement et choix en présence de points de vue multiples, RIRO No 8, VI. 1968.
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