The aim of this study is to solve the problem of labor allocation in cases where the company has fewer employees than the number of existing jobs based on the evaluation of work quality according to multiple objectives. Optimizing labor allocation not only benefits the company by maximizing the use of human resources, but also saves employees' energy. In addition, employees are assigned to tasks that match their skills and qualifications, maximizing their productivity. The research results show that the multi-objective decision-making algorithm based on the Hungarian algorithm is a suitable method to help leaders of companies solve the aforementioned problem text.
Tran, T. B.; Nguyen, H. H. P. Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm. Journal of Economic Statistics, 2023, 1, 15. https://doi.org/10.58567/jes01030002
AMA Style
Tran T B, Nguyen H H P. Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm. Journal of Economic Statistics; 2023, 1(3):15. https://doi.org/10.58567/jes01030002
Chicago/Turabian Style
Tran, Tram B.; Nguyen, Hien H. P. 2023. "Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm" Journal of Economic Statistics 1, no.3:15. https://doi.org/10.58567/jes01030002
APA style
Tran, T. B., & Nguyen, H. H. P. (2023). Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm. Journal of Economic Statistics, 1(3), 15. https://doi.org/10.58567/jes01030002
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References
Armstrong, M. (2009). Armstrong’s handbook of human resource management practice, 11th edition, Kogan Page Limited, London
Astuti, R. & Navi, M. (2018). Designing workload analysis questionnaire to evaluate needs of employees. AIP Conference Proceedings 1931, AIP Publishing, 030027, 1–8. https://doi.org/10.1063/1.5024086
Baeva, S; Komarevska. D; Nedeva, C; Todorov, T. (2009). Optimization of the human resource efficiency in companies. http://dx.doi.org/10.1063/1.3271621
Baron, A. & Armstrong, M. (1998). Performance Management: The new realities, Gardners Books, London, UK
Brewster, C., Mayrhofer, W. & Farndale, E. (2018). Handbook of Research in Comparative Human Resource Management, 2nd edition, Cheltenham: Edward Elgar Publishing
Greshilov, A.A. (2014). Mathematical Methods of Decision-making. Bauman Moscow State Technical University Press, Moscow
Haiyan Tu. (2019). Using data technology to optimize human resource management in colleges and universities. Advances in Social Science, Education, and Humanities Research 351. http://dx.doi.org/10.2991/mmetss-19.2019.161
Heinrich, C.J. (2002). Outcomes based performance management in the public sector: Implications for government accountability and effectiveness. Public Administration Review 62, 712–726. https://doi.org/10.1111/1540-6210.00253
Mammadova, M.G (1997). Decision-making, Based on a Knowledge Database with a Fuzzy Relational Structure, Elm press, Baku. https://doi.org/10.1007/978-3-319-75408-6
Mikoni, S.V. (2015). The Theory of Decision-making Management, “Lan” Press, Saint Petersburg, Russian
Kofman, A. (1982). Introduction to the fuzzy set theory. Radio and communication press, Moscow
Kuhn, H.W. (1955). The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly 2, 83–97. https://doi.org/10.1007/978-3-540-68279-0_2
Li, Yi & Linna, Wang (2022). A Genetic Algorithm Model for Human Resource Management Optimization in the Internet Marketing Era, ID:6931386. Mathematical Problems in Engineering, 1–9. https://doi.org/10.1155/2022/6931386
Liu, L. & Yong, S. (2022). Optimization of digital management path for human resource performance evaluation based on multiobjective decision-making mathematical model. Mobile Information Systems, ID:2604761, 1–9. https://doi.org/10.1155/2022/2604761
Rashid, K., Louis, J., Swanson, C. (2020). Optimizing Labor Allocation in Modular Construction Factory Using Discrete Event Simulation and Genetic Algorithm. Winter Simulation Conference (WSC), Orlando, FL, USA, 2569-2576. https://doi.org/10.1109/WSC48552.2020.9383867
Spencer, L.M., Spencer, S.M. (2008). Competence at Work Models for Superior Performance, Wiley India Pvt. Limited
Trakhtenherts, E.A., (2001). Possibilities and implementation of computer systems for decision-making support. Theary and systems management 3, 86-103.
Wang, J; Bai, W; Liu, Y. (2022). Optimization for human resource management strategy of the IoT industry based on AHP. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/3514285
Wang, X. & Zhang, Y. (2022). Enterprise human resource optimization algorithm using PSO model in big data and complex environment. Journal of Environmental and Public health, ID:1244660, 1–10. https://doi.org/10.1155/2022/1244660
Weiss, T. & Hartle, F. (1997). Reengineering performance management breakthroughs in achieving strategy through people, CRS Press Publication, USA
Zadeh, L.A. (1965). Fuzzy sets. Information and control 8(3), 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zongmin, L. Xuedong, L., Haidong, Y. (2017). Multi-criteria group decision making method for elevator safe evaluation with hesitant fuzzy judments. Appl. Comput. Math. 16(3), 296-312