Use decision matrix with all maximal criteria. Count
the normalised matrix :
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Alternative 1 |
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Alternative 2 |
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Alternative 3 |
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Alternative 4 |
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Alternative 5 |
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Alternative 6 |
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Alternative 7 |
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Alternative 8 |
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Alternative 9 |
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Alternative 10 |
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Ideal |
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Negative Ideal |
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Construct the weighted normalised decision matrix
with ideal and negative ideal variant:
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Alternative 1 |
0,036025
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0,023247
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0,002522
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0,00568
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0
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Alternative 2 |
0,022515
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0,014175
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0,001441
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0,00568
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0,029462
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Alternative 3 |
0,022515
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0,01134
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0,001081
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0,00568
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0,034661
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Alternative 4 |
0,019138
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0,008505
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0,001081
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0,00568
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0,038127
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Alternative 5 |
0,027019
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0,014175
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0,001585
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0,00568
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0,031888
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Alternative 6 |
0,011258
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0
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0,000144
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0,00568
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0,044192
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Alternative 7 |
0,027019
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0,020412
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0,002378
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0,006248
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0,020796
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Alternative 8 |
0,018012
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0
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0,000144
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0,00568
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0,044366
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Alternative 9 |
0,011258
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0,00567
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0,001081
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0,003976
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0,0409
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Alternative 10 |
0,019138
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0,002835
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0
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0,00568
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0,041593
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Ideal |
0,036025
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0,023247
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0,002522
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0,006248
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0,044366
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Negative Ideal |
0,011258
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0
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0
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0,003976
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0
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The best variant is the one with highest distance
from the negative ideal variant Dj.
Distance from
Dj
(relative index) |
Order | |
Alternative 1 | 0,535920422 | 6 |
Alternative 2 | 0,597909263 | 3 |
Alternative 3 | 0,596523934 | 4 |
Alternative 4 | 0,556886884 | 5 |
Alternative 5 | 0,661625922 | 1 |
Alternative 6 | 0,465764711 | 10 |
Alternative 7 | 0,63574363 | 2 |
Alternative 8 | 0,494854038 | 8 |
Alternative 9 | 0,492187412 | 9 |
Alternative 10 | 0,506997087 | 7 |