Sometimes, we want to integrate quantitative and qualitative criteria evaluations in a MADM problem. We consider a personnel selection problem. Age is a certain and quantitative criterion. Leadership is an uncertain and qualitative criterion. We know that AHP and similar methods are used quantitative and qualitative criteria evaluation together. However, If we evaluate these criteria seperately, how can we integrate quantitative and qualitative criteria evaluation?
Dear Ozcan
Maybe it is a problem in AHP but not in other methods.
Once yo place the performance values in the decision matrix there is no difference for most methods, except AHP and ANP
Some time ago a practitioner formulated the question as how to treat quantitative data in AHP, and he was given a lot of void words telling him that he must consider the essence of the data, and a lot of sentences without any sense, and then the poor guy did not receive any rational and especially practical answer
Apparently the process is dismiss the quantitative criteria and consider all of them as qualitative, which in my opinion is absurd, because in so doing the DM is overriding reliable and certain information, and replacing it with his own preferences
If the criteria calls for minimizing say costs, obtained from suppliers, it is beyond my comprehension how the DM can overide them
Curiously non AHP defender responded to my queries
Dear Bali,
The first approach is to select fuzzy numbers for quantifying the qualitative criteria. After which you can process to convert the qualitative criteria values to crisp values. Then, set-up a normalized decision matrix for the evaluation process. When normalizing the data sets, consideration must be given to the desired direction of each criterion (cost or benefit). The normalized data can be analyzed with any suitable MCDM tool of your choice.
Cheers.
Dear Desmond
I agree with what you said, there is no doubt that it is the right procedure, but our friend Ozacan is asking how to proceed when you have qualitative and quantitative criteria. It is obvious that it does not make sense to fuzzificate already crisp and reliable data
Dear Bali
I have expertise working on this kind problem, please let me know how I can help you
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