Research on robust recruitment optimization in interval-valued fuzzy evaluation environments

Abstract

 A precise recruitment can improve the efficiency of human resource management and enhance the core competitiveness of enterprises. However, the information asymmetry in recruitment leads enterprises to make recruitment decisions in fuzzy evaluation environments, thus reducing the accuracy of recruitment. This paper applies a robust approach to the recruitment optimization problem in an interval-valued fuzzy evaluation environment in which the actual abilities of applicants are randomly distributed within given intervals. The objective of this paper is to establish a robust recruitment scheme with the minimal maximum regret for recruitment revenue. Both exact and heuristic algorithms are proposed to solve the problem, which is proven to be NP hard. Computational experiments are conducted to evaluate the performance of the proposed algorithms. In addition, the paper reveals the key factors that affect the ability of an enterprise to implement accurate employment schemes. Corresponding suggestions on enterprise recruitment management are also proposed.