Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Abstract

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.