A Research On Two-sided Matching Algorithm Between New Hired Knowledge Workers And Position Requirements

The person-position matching problem is scholars’research hot spot. The degree of match directly affects the overall configuration of human resources effectiveness and production efficiency, thereby affecting the competitiveness of enterprises. The new hired knowledge workers do not understand the situation of new positions, making this a growing concern.In this paper, the new hired knowledge workers and positions are the object of study. An optimization policy is formulated to make the degree of satisfaction of both sides maximum, reaching the result that the position gets the right worker and the worker likes the position.First of all, arming to the index diversity of the degree that the position is satisfied to the new hired knowledge workers, the index is divided into three categories:0-1judging information index, interval number information index and language estimation information index. Triangular fuzzy number theory is cited to quantify each index. The degree that the position is satisfied to the new hired knowledge workers and its ranking can be calculated by this way.Secondly, the new hired knowledge workers do not understand the situation of the position; meanwhile, there is similarity between the positions. So it is difficult to distinguish which position he is most satisfied to. The intuition fuzzy approximate reasoning with the confidence factors will be proposed in this paper. Standard rule library is built to be compared with the data of position actual condition and the data that the new hired knowledge workers require. Then calculating the match degree of the new hired knowledge workers expect and the actual situation of the position.Finally, this article proposes the two-sided matching algorithm. Arming at maximizing the degree of satisfaction of both sides, it gets the best matching result. Effectiveness and efficiency of this method is tested by examples.