Dynamic person-position matching decision method based on hesitant fuzzy number information

Area of Science:

• Decision Sciences
• Operations Research
• Human Resource Management

Background:

• Traditional person-position matching often overlooks dynamic factors.
• The principles of 'fitting the person to the position' and 'fitting the position to the person' are increasingly important.
• Hesitant fuzzy numbers offer a robust way to handle uncertainty in evaluations.

Purpose of the Study:

• To propose a dynamic decision-making method for person-position matching using hesitant fuzzy numbers.
• To develop a stable matching model that accounts for evolving individual and position satisfaction.
• To optimize the selection of candidates for positions based on dynamic criteria.

Main Methods:

• Describing the dynamic person-position matching problem with hesitant fuzzy numbers.
• Calculating expected scores and satisfaction means for candidates and positions.
• Developing a stable matching model incorporating dynamic satisfactions and using generalized optimal order and correlation coefficients.

Main Results:

• An effective method for calculating missing correlation coefficients was presented.
• A novel calculation for dynamic satisfactions was proposed, integrating dominant and missing correlation coefficients.
• The proposed method yielded an optimal and stable person-position matching scheme.

Conclusions:

• The developed dynamic person-position matching method is feasible and effective.
• The model successfully establishes stable matching by considering dynamic satisfaction.
• This approach enhances decision-making in personnel selection and placement.