PROMETHEE II: Working principles

The PROMETHEE is a MCDA method created by J. P. Brans, who for the first time showed and explained this algorithm in 1982, during a conference titled “L’Ingéniérie de la Décision.

Elaboration d’instruments d’Aide à la Décision” at the Université Laval in Québec. Brans at first

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conceived the versions PROMETHEE I (partial ranking) and PROMETHEE II (complete ranking), which have been further developed during the years and applied to different fields, like banking, industrial location, manpower planning, water resources, investments, medicine, chemistry, health care, tourism, ethics, dynamic management [57]. As stated before, PROMETHEE II is an outranking method, in which different alternatives are evaluated and compared pairwise, according to a finite number of different criteria. PROMETHEE as all the other MCDA methods, is a procedure defined by consecutive steps which will lead us to the final ranking of alternatives and in our case, the sustainability assessment in BRI countries. At first, data should be collected, and the most representative criteria should be selected. Criteria should present all the necessary qualities for the energy and environmental decision problems, which are being systemic, consistent, independent, measurable and comparable. After that, weights should be assigned to all the selected parameters and in this regard, there are different possible methods to do so. The full list and the details of the chosen criteria and corresponding weights for our sustainability assessments are reported in the next chapter. Once all the information in input has been defined, the values of the characterising parameters should be collected in a matrix, as shown below.

CRITERIA

C1 C2 C3 C4

ALTERNATIVES

A1 s11 s12 s13 s14

A2 s21 s22 s23 s24

A3 s31 s32 s33 s34

A4 s41 s42 s43 s44

Table 15: Generic structure of data in input for PROMETHEE method.

In this structure for all the alternatives, generically identified with the letter A, there are the corresponding values of each criteria, marked with letter C. The data are in general called s for score and the numbers refers to the alternative and the criterion respectively.

The second step is to build difference matrices for each criterion. The difference matrix is a matrix in which pair-wise comparisons are performed and so, for each criterion, the difference between the scores of two alternatives is performed, as shown below for the generic k-th criterion. In this way, considering N criteria and M alternatives, there will be N matrices with the dimensions M x M and the main diagonal composed of zeros, since it represents the comparison and so the difference between the same alternative.

C_k A1 A2 A3 A4

A1 s1k-s1k s1k-s2k s1k-s3k s1k-s4k A2 s2k-s1k s2k-s2k s2k-s3k s2k-s4k A3 s3k-s1k s3k-s2k s3k-s3k s3k-s4k A4 s4k-s1k s4k-s2k s4k-s3k s4k-s4k

Table 16: Generic representation of a difference matrix for PROMETHEE method.

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Once all the difference matrices are calculated, it is the moment to implement the third step and create preference matrices for each criterion. The preference matrix is a matrix composed of values 𝑝_𝑖𝑗 , where i indicates the alternative and j the criterion as usual. The preference values define the distance of each pair-comparison from the best solution. There are different ways to define the criteria function, which is the relation that associate a preference value to each difference included in the difference matrices. The most used typologies are usual criteria, quasi criteria or U-shaped criteria, criteria with linear preference or V-shaped criteria, level criteria, criteria with linear preference and indifference or V-shaped with indifference criteria, and Gaussian criteria [57]. In our analysis we have decided to use the type 5, which is the V-shaped with indifference criteria, defined as follows:

𝑃(𝑑) = {

0, 𝑑 ≤ 𝑞

𝑑−𝑞

𝑝−𝑞, 𝑞 < 𝑑 ≤ 𝑝 1, 𝑑 > 𝑝

(1)

where d is the generic difference between two values and P(d) is the preference value associated to d, which is a number always between 0 and 1. In addition it is necessary to define for each criterion a preference value p and an indifference value q, which will be compared to the values of the difference matrices d. The preference value p defines an upper threshold for the criterion: if the difference between two scores, so a value in a difference matrix, is higher than the preference value p, it will have a value 1 in the preference matrix. That means that the comparison between two score is very close to the best and it has great importance in the decision process. On the contrary, the indifference value q indicates a lower limit for the criterion: if the difference between two scores is lower equal to the indifference value q, it will have a value 0 in the preference matrix. That means that the comparison between two score is not significant in the choice of the best alternative. If the difference between two scores is in between p and q, it will assume an intermediate value in the preference matrix, which can be calculated for example with a linear interpolation. The graphical representation of the criteria function used in shown in figure 24.

Figure 24: graphical and general representation of the type 5 criteria function for PROMETHEE.

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In the end, calculating all the P(d) for each difference for all the criteria, preference matrices are obtained, like the following one for the generic k-th criterion. Again, in the end, there will be the same number of preference matrices as the difference ones, so N matrices for N criteria with the dimension M x M alternatives.

C_k A1 A2 A3 A4

A1 p11 p12 p13 p14

A2 p21 p22 p23 p24

A3 p31 p32 p33 p34

A4 p41 p42 p43 p44

Table 17: Generic representation of a preference matrix for PROMETHEE method.

Then, the flows must be calculated which are needed to get to the final solution and raking of the alternatives. Flows can be positive or negative and they refer to the capability or not of an alternative to outrank or be outranked by the others, perform good or bad respectively. The positive flow represents the strength of each alternative while the negative one is its weakness [57]. Flows are contained in a R matrix, which is a matrix composed of the generic value r which is defined as follows:

𝑟_𝑖𝑗= ∑^𝑁_𝑘=1𝑃_𝑘(𝐴_𝑖 , 𝐴_𝑗) ∗ 𝑤_𝑘 (2)

So, considering the alternatives Ai and Aj and the k-th criterion, the generic value 𝑟_𝑖𝑗 is the sum of all preference values of each criterion Pk multiplied by the weight, generically called w, of the criterion considered. Finally, there will be a single matrix with the dimension M x M alternatives, since all the criterion will be considered together in a cell, where two alternatives are compared.

The positive flows are the sum by row of the elements of the R matrix, while the negative flows are the sum by columns. An example has been reported below.

R matrix A1 A2 A3 A4

Φ

A1 r11 r12 r13 r14

Φ

A2 r21 r22 r23 r24

Φ

A3 r31 r32 r33 r34

Φ

A4 r41 r42 r43 r44

Φ

Φ

Φ

Φ

Φ

Φ

Table 18: generic representation of a R matrix and positive and negative flows for PROMETHEE method.

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Finally, the net flow for the single alternative is calculated as the difference between positive and negative flows for the given alternative, as follows:

Φ_{𝑛𝑒𝑡,𝑗}= Φ_+,𝑗− Φ_−,𝑗 (3)

The net flow defines the final ranking of the alternatives, considering that the highest value determines the best alternative. In addition, it should be noted that the net flow can be positive or negative, since it is the balance between the outranking strength and weakness of each alternative.