Research on Optimization of Organizational Training Efficiency under Multiple Training Objectives Based on AHP-GA Algorithm
- DOI
- 10.2991/978-94-6463-264-4_89How to use a DOI?
- Keywords
- multiple training objectives; Genetic algorithm; Analytic Hierarchy Process
- Abstract
This paper proposes a solution that combines Analytic Hierarchy Process (AHP) and Genetic Algorithm (GA) to optimize the training duration of training institutions with multiple training objectives by establishing a mathematical model for optimizing organizational training benefits under multiple constraints. This method not only utilizes expert experience values to avoid the influence of extreme values, but also draws on the advantages of genetic algorithm to solve the optimal solution. Using AHP for quantitative and qualitative analysis to determine the weight of training target allocation, a combination of elite retention strategy and roulette wheel algorithm was used to select genetic operators, and simulation experiments were conducted to calculate the training duration allocation scheme with limited training duration and optimal training efficiency under multiple training objectives.
- Copyright
- © 2024 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 - Min Cao AU - Ping Luo AU - Ying Du AU - Yao Niu PY - 2023 DA - 2023/09/28 TI - Research on Optimization of Organizational Training Efficiency under Multiple Training Objectives Based on AHP-GA Algorithm BT - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023) PB - Atlantis Press SP - 769 EP - 779 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-264-4_89 DO - 10.2991/978-94-6463-264-4_89 ID - Cao2023 ER -