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

Jingbo Huang, Jiting Li, Yonghao Du, Yanjie Song, Jian Wu, Feng Yao () and Pei Wang ()
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Jingbo Huang: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Jiting Li: Academy of Military Sciences, Beijing 100071, China
Yonghao Du: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Yanjie Song: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Jian Wu: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Feng Yao: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Pei Wang: College of System Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2023, vol. 11, issue 23, 1-19

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.

Keywords: multi-level organization; human resource planning; network optimization model; improved late acceptance hill climbing algorithm; retrieval strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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