Download pdf
Optimization model for human resource allocation in the front office of banks
- 1 Wuhan Cogdel Cranleigh High School, Wuhan, China
* Author to whom correspondence should be addressed.
Abstract
Human resource allocation plays a pivotal role in enabling banks to adapt swiftly to financial market fluctuations and enhance operational efficacy. Addressing this challenge necessitates a comprehensive approach that accounts for various factors. To ensure optimal use of human resources, our research delves into essential considerations like position headcount, staff availability, employee competencies, and proficiency levels. Our objective is to formulate an integer optimization model, which strategically allocates personnel to distinct roles. Leveraging advanced optimization solvers, we solve this model to identify the most efficient staffing configurations. To validate our approach, we conducted a case study focusing on the front office of the Agricultural Bank of China. The results demonstrate the model's effectiveness in enhancing human resource allocation, ultimately contributing to the bank's operational efficiency and adaptability to market dynamics.
Keywords
optimization model, human resource, integer programming, bank
[1]. Du, S., & Ma, W. (2021). Deepening the demand for branch transformation and developing a new model for adaptive human resource allocation. China Financial Computer, (01), 62-64.
[2]. Ye, Z. (2024). Transformation strategies for bank human resource management in the context of financial technology. Business 2.0, (36), 69-71.
[3]. Song, F., & Thakor, A. (2022). Banking ethics, capital, and talent competition. Journal of Financial Intermediation, 52, 100963.
[4]. Wu, Y., Wang, S., Wang, X., et al. (2022). [Retracted] Application Research of Particle Swarm Algorithm in Bank Human Resource Management. Security and Communication Networks, 2022(1), 8788894.
[5]. Liu, Y. (2022). Research on human resource allocation in urban commercial banks. China Industrial Economy, (23), 132-134.
[6]. Meng, S., Feng, J., Zhao, R., et al. (2022). Current status and countermeasures of human resource allocation in commercial banks under digital transformation—A case study of N Bank A Branch. Modern Finance, (02), 9-16.
[7]. Wang, Z. (2023). Analysis of human resource optimization in private banks—A case study of Chouzhou Bank. Modern Enterprise Culture, (22), 153-156.
[8]. Yu, Y., Yue, X., Zhang, G., et al. (2024). Construction of job allocation and performance prediction model based on TS-BP algorithm. Information Technology, (12), 93-100+108.
[9]. Hao, W., Li, J., Shen, N., et al. (2024). Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model. Journal of Organizational and End User Computing (JOEUC), 36(1), 1-23.
[10]. Rokhforoz, P., Kebriaei, H., & Ahmadabadi, M. N. (2021). A fair task and time allocation of multiple human operators using decentralized Nash bargaining solution. Computers & Industrial Engineering, 153, 107027.
[11]. Pang, C. (2024). Improving human resource allocation efficiency in public institutions based on competency models. Human Resources, (10), 74-75.
[12]. Sinclair-Desgagné, B. (2021). Green human resource management–A personnel economics perspective. Resource and Energy Economics, 66, 101261.
[13]. Li, L., et al. (2023). On the Egalitarian–Utilitarian spectrum in stochastic capacitated resource allocation problems. International Journal of Production Economics, 262, 108900.
[14]. Luo, J., Zhuo, W., & Xu, B. (2023). The bigger, the better? Optimal NGO size of human resources and governance quality of entrepreneurship in circular economy. Management Decision.
[15]. Duan, Y., et al. (2024). Individualism and bank financial structure similarity. Journal of International Money and Finance, 148, 103151.
Cite this article
Liu,X. (2025). Optimization model for human resource allocation in the front office of banks. Journal of Applied Economics and Policy Studies,18(2),7-11.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Journal of Applied Economics and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).