Decision Making Method of Personnel-position Matching Based on Improved Probabilistic Linguistic ORESTE
With respect to the personnel-position matching in an information environment characterized by a multiattribute probabilistic linguistic set,a two-sided matching model is developed based on an improved ORESTE ranking method and matching aspiration.The proposed model introduces a probabilistic linguistic generalized Lance distance formula,employs a probabilistic linguistic power mean operator to determine the objective attribute weights,and optimizes the combination of subjective and objective weights based on the principles of game theory.This method overcomes the impact of extreme values on decision outcomes and ensures that attribute weights consider both the subjective analysis of decision makers'experiential judgments and the objective analysis of information structures,thereby enhancing scientific validity.Subsequently,the ORESTE ranking method is enhanced by incorporating the probabilistic linguistic generalized Lance distance formula and the Borda function,considering both weak and strong rankings.By simultaneously optimizing the combination of subjective and objective weights,the ranking results became more realistic and aligned with practical scenarios.To maximize satisfaction of the subjects'preferences,a new matching aspiration coefficient,embodying stability and based on the psychological"anchoring effect,"is proposed.This contributes to the construction of a rational and effective multiobjective two-sided matching model.The results of a case study involving personnel-job matching in a smart elderly care service platform demonstrate that the proposed two-sided matching model is effective and that decision-makers can adjust the parameters of κ based on their own risk preferences to maximize satisfaction with the subject's aspiration.Compared with decision methods such as ORESTE and TOPSIS,the proposed improved ORESTE matching model can obtain ranking values to obtain the optimal matching pair more reasonably and effectively.
万方数据
维普
