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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.

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