Research on Power Grid Position Allocation Decision Based on Multi-stage Modeling
- DOI
- 10.2991/978-2-494069-51-0_41How to use a DOI?
- Keywords
- position matching; multi-stage; random forest; management and decision-making
- Abstract
The advanced post matching system is of great significance for enterprise development and personal career development. We conduct a statistical survey on employees and posts of a power grid unit, and use the random forest under the multi-stage modeling strategy to study the relationship between individuals and posts. The research shows that the random forest under the multi-stage modeling strategy can effectively realize the job matching. On this basis, based on the characteristics of random forest discovery, such as the identification of the person in charge of the post, years of entry, age, years of termination of the labor contract, time of graduation from current education, professional and technical qualification level, etc., it has certain explanatory power for the post matching of the power grid.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Changjun Zhao AU - Xiaoyun Ding AU - Shunyu Deng AU - Zhiwei Tan AU - Gaixia Kang AU - Xiaogang Chen PY - 2022 DA - 2022/12/09 TI - Research on Power Grid Position Allocation Decision Based on Multi-stage Modeling BT - Proceedings of the 2022 7th International Conference on Modern Management and Education Technology (MMET 2022) PB - Atlantis Press SP - 298 EP - 305 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-51-0_41 DO - 10.2991/978-2-494069-51-0_41 ID - Zhao2022 ER -