# 117

Dear Lidija Kraujalienė

I read your article

Comparative analysis of multicriteria decision-making methods evaluating the efficiency of technology transfer

My comment

I understand the need and importance of your article. In reading Table 1. I notice that it is incomplete. There are problems that need more conditions to be modelled, thus, a MCDM method must:

1- Incorporate resources like money, tools, personnel, etc., as well as their availability.

2- Consider several scenarios simultaneously

3- Work with a portfolio of many different projects

4- Indicate when the proposed scenario is not feasible

5- Allow working with precedence between alternatives

6- Consider that alternatives may be inclusive or exclusive

7- Indicate the targets or goals of each criterion

8- Allow for special characteristics of each scenario

9- Permit execution of a rational sensitivity analysis, not based of weights of criteria

10- Indicate which of the total of existent criteria are significant for alternatives evaluation, since normally, not all of them participate.

11- Allow, considering only the participant criteria, in which range they can vary either increasing or decreasing, and the without affecting the position in the ranking of the best alternative

12- Work without weights. They exist in real problems, but we do not know them.

Subjective weights, even, when necessary, produce alteration of data, and they are useless for evaluation alternatives. However, a MCDM method must be able to work without weights, which means that the relative importance of criteria must be taken into account by the method in its own algorithm, that is, computed without human intervention, or by using objective weights. However, the opinion of the DM is important, and so, if desired, thus his/her estimates must also be considered, and the method must accept them

13- Allow the DM to establish his/her preferences for both, alternatives an criteria

14- Offer the possibility for the DM to make a comparison between the computed target and the wished target. This will inform the DM in which extent an objective is satisfied. If the DM is not happy with it, he can modify data to reach this objective

15- Be able to consider especial conditions established by the DM, for instance, that each alternative must satisfy as a minimum, a certain number of criteria

16- In case of a large number of alternatives, the method must allow the DM to establish the final reduced number of alternatives wished.

17- Be able to use different types of normalization.

18- Contemplate cases where not all alternatives start and finish at the same time

19- Be able to draw the total utility curve for each objective

20- Allow clusters

21- Permit group working

Is there any MCDM method that can perform all of these requirements and those in Table 1?

Yes.

Dear author, if youor any other perdson are interested and need information or access to this method, which is free, please contact me

I hope this can help

Nolberto Munier