Multi-Criteria Decision Making in Personnel Selection

Multi-criteria decision making (MCDM) in personnel selection addresses the inherent complexity of evaluating candidates against a range of often conflicting attributes. The process typically comprises the identification of relevant criteria—such as technical expertise, behavioural competencies and cultural fit—the determination of criterion weights, the aggregation of candidate scores and the ranking of applicants to support final hiring decisions. Traditional MCDM methods include the analytic hierarchy process, which decomposes complex decisions into hierarchical structures, and distance-based techniques like TOPSIS, which assess proximity to ideal and anti-ideal solutions. To accommodate uncertainty and subjective judgement, extensions leveraging fuzzy logic, neutrosophic sets and grey relational analysis have been proposed. More recently, entropy-based approaches and data-driven frameworks that integrate machine learning have emerged to reduce bias and enhance transparency. These methodologies have found practical application across sectors—from multinational corporations to public service organisations—underscoring the global importance of systematic, evidence-based recruitment processes. Effective MCDM frameworks not only improve decision quality but also contribute to workforce diversity, reduce recruitment costs and optimise organisational performance.

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Multi-Criteria Decision Making in Personnel Selection publication trend

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Technical terms

Analytic hierarchy process (AHP): A method that organises decision problems into a hierarchy of criteria and subcriteria, using pairwise comparisons to derive relative weights.

TOPSIS: A technique for order preference by similarity to an ideal solution, ranking alternatives by their distances from ideal and anti-ideal points.

Neutrosophic set: An extension of fuzzy set theory characterised by three independent membership functions—truth, indeterminacy and falsity—to model uncertainty and incomplete information.

Entropy weight method: A data-driven approach that determines criterion weights based on the variability of performance scores, with higher entropy indicating greater discriminative power.

Grey relational analysis: A method for handling systems with partially known information by quantifying the strength of relationships between data sequences under uncertainty.

References

1. Enhancing Personnel Selection through the Integration of the Entropy Synergy Analysis of Multi-Attribute Decision Making Model: A Novel Approach. Information (2023).
2. An Integrated Neutrosophic-TOPSIS Approach and Its Application to Personnel Selection: A New Trend in Brain Processing and Analysis. IEEE Access (2019).
3. A data-driven MADM model for personnel selection and improvement. Technological and Economic Development of Economy (2020).