GA-GDEMATEL: A Novel Approach to Optimize Recruitment and Personnel Selection Problems

Abstract

The complexity of human resource management (HRM) requires an integrated method of subjective and objective evaluation rather than intuitive decisions such as multicriteria decision-making (MCDM). This study proposes a hybrid Genetic Algorithm and Decision-Making Trial and Evaluation Laboratory (GA-GDEMATEL)-based grey theory systems approach to solve personnel selection problems in a real-case study from a Vietnamese agriculture manufacturing and services corporation. First, the GDEMATEL approach is deployed to investigate the causal relationship between the proposed criteria and determine the subjective weights of recruitment criteria. Second, the GA model utilizes selection, crossover, and mutation with a new objective function of Minimizing Distance to Ideal Solution (MDIS) to find the optimal solution for robust recruitment based on GDEMATEL weights. Notably, the GA-GDEMATEL could be exploited effectively in a short time to optimize personnel selection in "deep and wide"aspects. Moreover, the study's findings on recruiting evaluation and selection problems provide a support model and new research perspectives to the literature and help managers achieve the best solution by dealing with qualitative and quantitative criteria more effectively.