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Up to now, our research, based on the theory of fuzzy subsets, leads to an alternative implementation of a series of techniques arising from multivalent logics [1] to decision making in management of human resources, using known algorithms (like Hamming [2], Euclean [3], Tran y Duckstein [4] distances, adequation coefficient [5] or weighted mean hemimetric for fuzzy numbers [6]) even creating new instruments (such as the “maximum and minimum level index” [7] or “discarding by overcoming rate-distance index” [8]) which would allow us to adapt in an even more reliable manner to the always complex and unstable reality. In many cases, in order to avoid uncontrollable errors, the expert was advised, if in doubt, to work with confidence intervals in stead of crisp numbers which, allowed to limit the uncertainty to a greater degree and to do calculations that ensured that we avoided errors in estimates or valuations.  The results were satisfactory and, besides, it was possible to approach a new question: if experts could carry out their valuations in a more meticulous way if possible (by providing several possibilities between a minimum and maximum), valuing, for example, by means of fuzzy numbers [9], would it be feasible to operate in order to find in the end, Hamming distances with weighting? The answer is yes. Many manuals provide us with the techniques to, on the one hand, calculate distances and, on the other hand, operate with fuzzy numbers. But to find distances between fuzzy sub-sets of a degree α (for example) was a challenge which we have allowed ourselves to tackle.

The treatment of fuzzy and specific information provided by experts for decision making in the selection of workers