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1516<br />
Table 1<br />
Standard prequalification criteria and weights<br />
Criteria<br />
Work experience<br />
Technology and equipments<br />
Management<br />
Experience and knowledge of the operation<br />
team<br />
Financial stability<br />
Quality<br />
Being familiar with the area or being domestic<br />
Reputation<br />
Creativity and innovation<br />
Construction<br />
Water<br />
Transportation<br />
Industry<br />
Energy<br />
Utilities<br />
Geology<br />
Tele<br />
communication<br />
Agriculture<br />
Service<br />
10 10 10 10 10 10 10 10 10 10<br />
20 20 20 20 20 20 20 20 20 20<br />
10 10 20 5 10 5 20 0 10 10<br />
20 20 30 10 20 10 40 5 20 20<br />
2 2 2 2 2 2 2 2 2 2<br />
5 5 5 5 5 5 5 5 5 5<br />
10 10 10 10 10 10 10 10 10 10<br />
20 20 20 20 20 20 20 20 20 20<br />
20 20 20 20 20 20 20 20 20 20<br />
30 30 30 30 30 30 30 30 30 30<br />
5 5 5 10 5 10 5 15 5 5<br />
10 10 10 20 10 20 10 20 10 10<br />
10 10 10 0 0 5 10 5 10 20<br />
20 20 20 10 10 10 20 10 20 30<br />
10 10 10 10 10 10 10 10 10 10<br />
10 10 10 10 10 10 10 10 10 10<br />
5 5 5 5 5 5 5 5 5 5<br />
15 15 15 15 15 15 15 15 15 15<br />
100 points is the maximum score for ideal contractor, which gets at the prequalification stage and the<br />
threshold value for elimination is 65, for criteria 2,4,5 minimum of 40% is needed. Respecting the<br />
main criteria, to calculate the score of application, management and planning organization proposed<br />
simple additive weighting method (SAW). Hwang and Yoon (1981) referred the decision maker<br />
(DM) assigns importance weights to each of the property in SAW, which becomes the coefficients of<br />
the variables. By multiplying the scale rating for each attribute value by the importance weigh and<br />
then summing these products over all attributes, DM obtains a total score for each alternative. SAW<br />
is very powerful method among MADM techniques.<br />
4. An integrated VIKOR and AHP methodology<br />
Basic definitions of fuzzy sets (Chen, 2000):<br />
A fuzzy number is a fuzzy subset in the universe of discourse X that is both convex and normal. Fig.<br />
1 shows a fuzzy number τ ̃ of the universe of discourse X which is both convex and normal.<br />
The -cut of a fuzzy number τ ̃ is defined,<br />
̃ : τ ̃ , , (1)<br />
where [0, 1], τ ̃ is a non-empty bounded closed interval contained in X and it can be denoted by<br />
τ α , τ α , and τ <br />
α<br />
and <br />
<br />
are the lower and upper bounds of the closed interval, respectively.