5.  Multiple Criteria Decision Making /Finite Number of Alternatives/

5.1.  Introduction

Multiple criteria decision-making (MCDM) refers to making decisions in the presence of multiple - usually conflicting criteria. Problems for multiple criteria decision making are common occurrences in everyday life and many examples can be found.
Examples of MCDM
Decision is understood as choice of one variant from certain list of real possible variants. This chosen variant is considered to be optimal but often designation what is optimal is the most difficult part of problem. We have to take in mind that many criteria are contradictory and interests of various subject of decision making can be in conflict (shop owner x customer). Even one person cannot some times make his/her decision without inner conflict.
Another problem is quantification of available information. Some input information are quantitative (price of new shoes – we get precise number expressed in money units) and some kinds of information are not directly quantitative and must be transferred into numbers (beauty, modishness, durability etc.) There are many methods for doing that but each has advantages and disadvantages and certain influence of subjective opinion is not excluded.
Example: School Grades
Belief in quantitative data generally depends on local tradition and type of solved problem. Many people still prefer intuition and experience. The true is that there are no methods (and never will be) for algorithmisation of whole decision process but using quantitative methods can lay aside big mistakes (dominated variants – see latter).
The theory on mathematical modelling, but some parts don’t require deep mathematical knowledge and could be study independently. Especially exploitation of some software is simple. On the other hand deeper knowledge of MCDM methods including mathematical background leads to more serious access and better interpretation of results. This text contains classification of MCDM methods, characteristic of each class of methods. Detail description is supplemented only to several best known and widely used methods.
The aim of studying this text is not to be familiar with each existing method (impossible due to high number of methods and continual development of new), but understanding of basic principles and ability to use some methods (via software) and explain results.

5.2. Basic Concepts

All MCDM problems have the following common characteristics:
Multiple objectives/attributes
Each problem has multiple objectives/ attributes. A decision-maker must generate relevant objectives/attributes for each problem setting.
Conflict among criteria
Multiple criteria usually conflict with each other (for example the quality and price of goods).
Incommensurable units
Each objective/attribute has a different unit of measurement. In the car selection case, gas mileage is expressed by miles per gallon (MFG), comfort is by cu ft if fit is measured by passenger space, safety may be indicated in a nonnumeric way, cost is indicated by dollars, etc.
Solutions to these roblems are either to design the best alternative or to select the best one among previously specified finite alternatives.

There exist two alternative sets due to the different problem settings: one set contains a finite number of elements (alternatives), and the other has an infinite number.
The problems of MCDM can be broadly classified into two categories in this respect:
Multiple Attribute Decision Making - MODM (in this chapter)
Multiple Objective Decision Making – MADM (in following chapter)

Multiple objective decision-making (MODM) is not associated with the problem where the alternatives are predetermined. The thrust of these models is to design the 'best' alternative by considering the various interactions within the design constraints which best satisfy the decision maker by way of attaining some acceptable levels of a set of some quantifiable objectives.

The common characteristics of MODM methods are that they possess:
1) a set of quantifiable objectives
2) a set of well defined constraints
3) a process of obtaining some trade-off information, implicit or explicit, between the stated quantifiable objectives and also between stated or unstated nonquantifiable objectives.
MODM is associated with design problems (in contrast to selection problems for the MADM).

In MADM, there are usually a limited (and countable) number of predetermined alternatives. The alternatives have associated with them a level of the achievement of the attributes (which may not necessarily be quantifiable) based on which the final decision is to be made. The final selection of the alternative is made with the help of inter- and intra-attribute comparisons. The comparisons may involve explicit or implicit trade-off.

5.2.1. Terms and Definitions

Criteria - A criterion is a measure of effectiveness, basis for evaluation.
Goals – A priori values or levels of aspiration. These are to be either achieved or surpassed or not exceeded.
Attributes – Each alternative can be characterised by a number of attributes = characteristics = parameters = factors = properties.
Objectives - Something to be pursued to its fullest, generally direction of planned change. For example the farmer wants to maximise his profit or minimise production costs or minimise the water pollution.
Decision matrix – The MADM problem can be expressed in a matrix containing criteria in columns and variants in lines. Elements xij indicate evaluation of alternative i with respect to criterion j.
Hence variant ai, i= 1,2,...m
Is denoted by line vector
and column vector
shows the contrast of each alternative with respect to attribute fj.

        Decision (Criteria) Matrix for MADM Model

Example:Washing machines - Decision Matrix

Transformation to All Maximal Criteria - Many MADM methods suppose that all criteria are maximising. To be in line with this demand we must transfer minimising criteria to maximising. For example minimising criterion ”Price” we can transfer to maximising criterion ”Saved money”. The most expensive variant will have zero in the ”Saved money” column and other – cheaper variants – the value one can save comparing with the most expensive variant.
Example: Washing Machines – Criteria Transformation

Dominated variant – Variant  is dominating variant

(dominated variant) if

Nondominated variant – Variant ai is nondominated if the decision set of variants A doesn’t contain variant that dominates ai. The set of nondominated variants from the set A is An.
Ideal variant – Hypothetical or real variant that is best in all criteria (or as good as the best variant).
Basal variant – Hypothetical or real variant that is worst in all criteria.
Graphical Presentation of MADM Model
Graphical Presentation of Nondominated Variants
Graphical Presentation of Dominated Variants
Example: Washing Machines – Graphical Presentation
Satisfying solutions - A satisfying solution is a reduced subset of the feasible set which exceeds all of the aspiration levels of each attribute. A set of satisfying solutions is composed of acceptable alternatives. A satisfying solution may well be used as the final solution though it is often utilised for screening out unacceptable solutions.
Preferred solution - A preferred solution is a nondominated solution selected as the final choice.
Noncompensatory Model
These models do not permit trade-offs between attributes. An advantage or favourable value in some other attribute cannot offset a disadvantage or unfavourable value in one attribute.
Hence comparisons are made on an attribute-by-attribute basis. The MADM methods that belong to this model are credited for their simplicity. They are dominance, maximin, maximax, conjunctive constraint method, disjunctive constraint method, and lexicographic method.
Compensatory Model
Compensatory models permit trade-offs between attributes. That is, in these models changes (perhaps small changes only) in one attribute can be offset by opposing changes in any other attributes. With compensatory models a single number is usually assigned to each multidimensional characterisation representing an alternative. Based upon the principle of calculating this number, compensatory models can be divided into three subgroups.
Example:Washing Machines – Subgroups of MADM Models

5.2.2. Normalisation

A normalisation may be essential for some methods to facilitate the computational problems inherent to the presence of different units in decision matrix. The advantage of normalisation is that all criteria are measured in dimensionless units and inter-attribute comparison is possible. The normalisation procedure doesn’t lead to equal minimum and maximum values for all attributes, so straightforward comparison is still difficult.
There are different methods of normalisation, some of then will be mentioned together with particular MADM method.
Vector normalisation
This procedure implies that each row in decision matrix is divided by its norm. The elements of normalised matrix R are calculated:

Linear scale transformation
Another possibility is to divide the outcome of certain criterion by its maximum value:

Some MADM methods has it’s own formulas for criteria normalisation i.e. (5.20).

5.2.3. Weights Assessing

Many methods require information about the relative importance of each attribute (or objective). It is usually given by a set of (preference) weights, which is normalised to sum to 1. In case of n criteria, a set of weights is

There are several techniques for designation the criteria weights. The main difference is if we collect the judgements from one or more persons.

Sequence Method
The decision-maker should be able to arrange criteria according their importance to a sequence from most to least important. A natural numbers from k to 1 are assigned to criteria (most important criterion number k, second most important k-1 etc.). The weights are calculated as follows:

Sum of bi is equal to sum of first k natural numbers

Score Method
This methods counts with judgement of criteria importance by score. The decision-maker evaluates each criterion by certain number of points from chosen scale ( for example 1-100). More important criterion has higher score ( more points). The weight calculations are than made according (5.6).
Example: Washing Machines – Assessing Weights

Saaty’s Matrix

The decision-maker is supposed to judge the relative importance of each pair of criteria. The elements of matrix are considered to be an estimation of importance of i-th and j-th criteria. The scale 1,3,5,7,9 is often used.

Verbal Explanation of the Scale

This matrix is called Saaty’s matrix. This is a 'reciprocal matrix’ that has all positive elements and has the reciprocal (a) and consistency (b) property:

Saaty introduced a method of scaling ratios using the principle eingenvector of positive pairwise comparison.
Eingenvector method
Weighted Least Square Method (logarithmic)
Consider the elements sij of Saaty's matrix S in (5.6). It is desired to determine the weights wj, such that, given .

The estimations of weights are designated by minimisation of the quadratic form:

Solution of (5.10) is normalised geometrical average of lines of matrix S:

Numerical Example Weighted Least Square Method for Assessing Weights

LINMAP (Linear Programming Techniques for Multidimensional Analysis of Preference) is a method for assessing the weights of attributes as well as for selecting the alternative.

5.3. Methods for Multiple Attribute Decision Making

5.3.1. Classification of MADM Methods

The available input information varies. One may not indicate his preferences at all, or may represent his preference through the form of attribute or alternative. The degree of judgement skill also varies. For instance, we may list the different preference information on attributes by the ascending order of complexity: standard level, ordinal, cardinal, and marginal rate of substitution.

We classify methods for MADM based upon different forms of preference information from a decision-maker.

1. Class – Methods of No Information Given

2. Class – Methods for Information on Attribute Given
2.1. Methods for Standard level of Attribute Given
Disjunctive Method
Conjunctive Method

2.2 Methods for Ordinal of Attribute Given
Lexicographic Method
Elimination by Aspects
Permutation Method

2.3 Methods for Cardinal Information of Attribute Given
Linear Assignment method
Simple Additive Weighting Method
Hierarchical Additive Weighting Method

2.4 Methods for Marginal Rate of Substitution of Attribute Given
Hierarchical Trade-offs Method

3. Class - Methods for Information on Alternative Given

3.1 Methods for Pairwise Preference Information Given
Interactive Simple Additive Weighting Method

3.2 Method for Order of Pairwise Proximity Information Given
Overview of MADM Methods
Description of some of these methods follows.

If there is no information on criteria importance we have to use one of the method of 5.4 chapter.
If preference information either on attributes or alternatives is available, the information can be expressed in various ways:
1) standard level of each attribute (chapter 5.5)
2) relative importance by ordinal preference (chapter 5.6)
3) relative importance by cardinal preference (chapter 5.7)
4) marginal rate of substitution between attributes

5.4. Methods without Preference Information

5.4.1. Dominance

An alternative is dominated if there is another alternative, which excels it in one or more attributes and equals it in the remainder. Eliminating the dominated ones can reduce the number of alternatives. A set of nondominated solutions is one obtained through the sieve of dominance method. There are no assumptions so we don’t need to transfer minimising criteria to maximising. Steps of the method:

Example: Colour Films – Discarding Dominated Variants

5.4.2. Maximin

Under this procedure only a single weakest attribute represents an alternative; all other (n-1) attributes for a particular alternative are ignored. If these lowest attribute values come from different attributes, as they often do, we may be basing our final choice on single values of attributes that differ from alternative to alternative. Therefore, the maximin method can be used only when interattribute values are comparable; that is, all attributes most be measured on a common scale; however, they need not be numerical.

The alternative, A +, is selected such that:
The common scale for all criteria can be obtained using the degree of closeness to the ideal solution; the ratio of an attribute value xij to highest attribute value X*j:
Example: Colour Films MAXIMIN

5.4.3. Maximax

The maximax method selects an alternative by its best attribute value. In this case the highest attribute value for each alternative is identified and these maximal value are compared. The alternative with highest such value is to be chosen. All criteria must be measured in the same scale, if not use (5.13) or more complicated formula (5.3) or other formulas designed for criteria transformation.

The alternative, A +, is selected such that:


  Example: Colour Films MAXIMAX

5.5. Methods for Standard Level of Attribute Given

The decision-maker sets the minimal attribute values (standard levels) he/she will accept for each attribute. Any alternative that has an attribute value less than standard level will be rejected (is not acceptable). This procedure is called conjunctive method. If the variant is accepted in the case at least one attribute is over or equal to standard level the method is called disjunctive.

5.5.1. Conjunctive Method

We classify Ai as an acceptable variant only if

    where aj0 is the standard level of aj.
The conjunctive (satisfying) method is not usually used for selection of alternatives but rather for dividing them to acceptable and not acceptable.
An alternative is evaluated on its greatest value of an attribute. For example, professional football players are selected according to the disjunctive method. A player is selected because he can either pass exceptionally, or run exceptionally, or kick exceptionally etc.

We classify Ai as an acceptable alternative only if:

    where aj0 is the desirable level of aj.
5.6. Methods for Ordinal Preference of Attribute Given

The most important information needed is the ordinal interattribute preference information. The relative importance among attributes determined by ordinal preference is less demanding for the decision-maker to assess than by cardinal preference.

5.6.1. Lexicographic Method

In some decision situations a single attribute seems to predominate. One way of treating this situation is to compare the alternatives on the most important attribute. If one alternative has a higher attribute value than any of the other alternatives, the alternative is chosen and the decision process ends.

However, if some alternatives are tied on the most important attribute, the subset of tied alternatives are then compared on the next most important attribute. The process continues sequentially until a single alternative is chosen or until all n attributes have been considered.

Example:Lexicographic method

5.6.2. Permutation Method

The method consists of testing each possible ranking of the alternatives against all others. With m alternatives, m! permutation rankings are available, so it is not convenient for big models. The method will identify the best ordering of the alternative rankings, then the dominating alternative. The method can be used also if the cardinal preferences are known.

If there are only ordinal preferences we can define cardinal preferences using for example Sequence Method (see chapter 5.2.3).

Case of cardinal preferences

Suppose a number of alternatives A1,…,Am have to be evaluated according to a attributes f1,…fn. Assume that a set of cardinal weights v1,…,vn be given.
Let’s have three alternatives: A1, A2, and A3. Then six permutations of the ranking of the alternatives exist (m! = 3! = 6). They are:

P1=A1,A2,A3     P3=A2,A1,A3     P5=A3,A1,A2

P2=A1,A3,A2     P4=A2,A3,A1     P6=A3,A2,A1

For each permutation we compare all pairs of variants. If in the partial ranking of variants Ak and Al under j-th criterion is we put the criterion weight vj into set Ckl, if we are put the vj to the set Dkl.
Then the evaluation criterion of permutation Pi is given by:

The best permutation is that with maximal value of sum of Ri.

Example:Fighter Aircraft Problem – Permutation Method

5.7. Methods for Cardinal Preference of Attributes

The methods in this class require the cardinal preferences of attributes. This (a set of weights for the attributes) is the most common way of expressing the interattribute preference information.

5.7.1. Linear Assignment Method

The linear assignment method is based on a set of attribute-wise rankings and a set of attribute weights. The method features a linear compensatory process for attribute interaction and combination. In the process only ordinal data, rather than cardinal data, are used as the input. This information requirement is attractive in that we do not need to scale the qualitative attributes.

The method is to pick the sum of ranks for each alternative and rank them from the lowest sum to the highest sum. For instance, consider the following attributewise preferences with equal weight:

Here we obtain: rank (A1) = 1 + 1 + 2 = 4, rank (A2) = 6, and rank (A3) = 8.
Thus in spite of its apparent simplicity, this method fails to meet our basic linear compensation requirement.
A compensatory model from this simple approach is devised. Let us define a product-attribute matrix n as a square (m x m) nonnegative matrix
whose elements Rik represent the frequency (or number) that Ai is ranked the kth attributewise ranking. For example, the corresponding matrix with equal weights on the attributes is
It is understood that  measures the contribution of Ai to the overall ranking, if Ai is assigned to the kth overall rank. The larger  indicates the more concordance in assigning Ai to the kth overall rank. Hence the problem is to find Ai for each k, k = 1,2,...,m, which maximises .
The linear assignment method can be written as a following LP model:
(pij is an element of permutation matrix P (m x m), pik=1 if Ai is assigned to overall rank k, otherwise pik=0)

Example:Fighter Aircraft Problem – Linear Assignment Method

5.7.2. Simple Additive Weighting Method

Simple Additive Method (SAW) is probably more often used method of MADM.
The method is based on maximal the trade-off of variants. The simplification is established by considering only linear function of trade-off.
The method requires all maximal criteria, the same scale for and existing weights’ vectors.
Multiplying the scale rating for each attribute value by importance weight assigned to the criterion and then assuming these products over all criteria obtain total trade-off u (ai) for each alternative.

All criteria must be measured in the same scale, if not use (5.20)

Example: Washing Machines – SAW

5.7.3. TOPSIS

(Technique for Order Preference by Similarity to Ideal Solution)

TOPSIS takes the cardinal preference information on attributes. The ideal solution is guaranteed to have the longest distance to the negative ideal solution.

TOPSIS assumes that each attribute in the decision matrix takes either monotonically increasing or monotonically decreasing utility. In other words, the larger the attribute outcomes, the greater the preference for the "benefit" criteria and the less the preference for the "cost" criteria. Further, any outcome, which is expressed in a nonnumeric way should be quantified through the appropriate scaling technique. Since all criteria cannot be assumed to be of equal importance, the method receives a set of weights from the decision-maker.

The proposed method will be presented in steps:

(Assumption: All maximal criteria see 5.1.1)

Step 1. Construct the normalised decision matrix
This process tries to transform the various attribute dimensions into non-dimensional attributes, which allows comparison across the attributes. An element rij of the normalised decision matrix R can be calculated as:

After this transformation each attribute has the same unit length of vector.

Step 2. Construct the weighted normalised decision matrix
This matrix can be calculated by multiplying each column of the matrix R with its associated weight vj. Therefore, the weighted normalised decision matrix W is equal to:

Step 3. Determine ideal and negative-ideal solutions
Let two artificial alternatives Hj and Dj be defined as:

Step 4. Calculate the separation measure
The separation of each alternative from ideal and negative-ideal solution can be measured by Euclidean distance:

Step 5. Calculate the relative closeness to the negative- ideal solution
The relative closeness ci of i-th variant to negative ideal variant is defined:

The variant that is closest (ci close to 0) to the negative ideal variant has the highest distance from ideal variant and is taken as worst variant. Variant with highest distance from negative ideal variant (ci close to 1) is closest to ideal variant and can be chosen as best.

Step 6. Rank the Preference Order
The variant that is closest ( ciclose to 0) to the negative ideal variant has the highest distance from ideal variant and is taken as worst variant. Variant with highest distance from negative ideal variant (ci close to 1) is closest to ideal variant and can be chosen as best.

A set of alternatives can now be ranked according to descending order of ci.

Example:Washing Machines – TOPSIS

5.7.4. Electre

(Elimination et Choice Translating Reality)

ELECTRE uses the concept of an 'outranking relationship'. The outranking relationship of says that even though two alternatives k and l do not dominate each other mathematically, the decision maker accepts the risk of regarding Ak as almost surely better than Al.

This method consists of a pairwise comparison of alternatives based on the degree to which evaluations of the alternatives and the preference weights confirm or contradict the pairwise dominance relationships between alternatives. It examines both the degree to which the preference weights are in agreement with pairwise dominance relationships and the degree to which weighted evaluations differ from each other. These stages are based on a 'concordance and discordance' set; hence this method is also called concordance analysis.

The ELECTRE method takes the following steps:

Step 1. Calculate the normalised decision matrix –see (5.21) TOPSIS

Step 2. Calculate the weighted normalised decision matrix – see (5.22) TOPSIS

Step 3. Determine the concordance and discordance set
For each pair of alternatives the set of decision criteria is divided into two distinct subsets. The concordance set Ckl for Ak and Al is composed of all criteria for which Ak is preferred to Al. The complementary subset is called discordance set.

Step 4. Calculate the concordance matrix
The relative value of the concordance set is measured by concordance index, which is equal to the sum of weights associated with criteria contained in the set. The concordance index for normalised weight set is:


 The successive values of concordance idices ckl (k,l = 1,2,…,m; k? l) form the concordance matrix C (m x m):

Step 5. Calculate the discordance matrix
The concordance index reflects the relative dominance of a certain alternative over a competing alternative on the basis of the relative weight attached to the successive decision criteria. So far no attention has been paid to the degree to which the evaluations of a certain Ak are worse than the evaluations of competing AR. Therefore a second index, called the discordance index, has to be defined:


Higher value of dkl implies that, for discordance criteria (=criteria preferring Al) , Al is more favourable that Ak; lower value of dkl Al is less favourable than Ak.

The discordance idices form the discordance matrix D (m x m):

Step 6. Determine the concordance dominance matrix
Ak will only have a chance of dominating Al , if its corresponding concordance index ckl exceeds at least a certain threshold value i.e. .

The threshold value can be determined as:

On the basis of a threshold value a Boolean matrix F can be constructed:

Each ”1” in matrix F represents a dominance of one alternative over another.

Step 7. Determine the discordance dominance matrix
This matrix is constructed in a way analogous to the F matrix on the basis of a threshold value d to the discordance indices. The elements of gkl of the discordance dominance matrix G are calculated as:


Also the unit elements in the G matrix represent the dominance relationships between any two alternatives.

Step 8. Determine the aggregate dominance matrix
The next step is to calculate the intersection of the concordance dominance matrix F and discordance dominance matrix G. The resulting matrix, called the aggregate dominance matrix E, is defined by means of its typical elements ekl as:


Step 9. Eliminate the less favourable alternatives
The aggregate dominance matrix E gives the partial-preference ordering of the alternatives. If ekl = 1, then Ak is preferred to Al for both the concordance and discordance criteria, but Ak still has the chance of being dominated by the other alternatives. Hence the condition that Ak is not dominated by ELECTRE procedure is:


 This condition appears difficult to apply, but the dominated alternatives can be easily identified in the E matrix. If any column of the E matrix has at least one element of 1, then this column is 'ELECTREcally' dominated by the corresponding row(s). Hence we simply eliminate any column(s) which have an element of 1.

Example: The Fighter Aircraft Problem – ELECTRE

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Fiala P.,Jablonský J., Maňas M.: Vícekriteriální rozhodování,VŠE Praha 1997
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