Competency-based selection and assignment for project manager in Iranian railways projects by genetic multigenic programming

Mehdi VakilZadeh; Mohsenali Shayanfar; Masoud Zabihi-Samani; Mehdi Ravanshadnia

doi:10.31181/dmame622023727

Authors

Mehdi VakilZadeh Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-1397-394X
Mohsenali Shayanfar Department of Civil Engineering, Iran University of Science & Technology, Tehran, Iran https://orcid.org/0000-0001-6358-2771
Masoud Zabihi-Samani Department of Civil Engineering, Parand Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-1330-165X
Mehdi Ravanshadnia Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-4990-7127

DOI:

https://doi.org/10.31181/dmame622023727

Keywords:

Project management, active personnel in the railway industry, competency-based selection, genetic programming, multigene regression

Abstract

As a part of human resource management, active companies in Iran's railway industry must determine the qualifications for appropriate project managers in railway construction. This research aims to consider the importance of Iran's railway construction projects due to the lack of staff with expertise and their high economic impact and budget. A decision-making model is presented. The project manager's qualifications are determined through questionnaires answered by experts in the field. The numerical model is created to determine the qualifications for project management in the railway industry. Therefore, Competency-Based Selection by Genetic Programming Multigenic Regression (CSPR) is proposed for project-oriented human resource management. Decision-making is done in three phases: Hierarchical analysis process for evaluating and determining the qualifications based on the questionnaires. The model is trained and validated by creating optimal coefficients and genes to determine the contribution of each of these qualifications in determining each candidate's final qualifications based on experts' questionnaires. Finally, the model's accuracy with optimal coefficients will be tested based on the questionnaires not used in the model training phase. The CSPR model provides a qualitative method and numerical optimization to evaluate and grade the project manager in Iran's railway construction projects based on the metaheuristic method.

Downloads

Download data is not yet available.

References

Ali, H., Chuanmin, S., Ahmed, M., Mahmood, A., Khayyam, M., & Tikhomirova, A. (2021). Transformational leadership and project success: serial mediation of team-building and teamwork. Frontiers in Psychology, 12, 3698. https://doi.org/10.3389/fpsyg.2021.689311

Belout, A., & Gauvreau, C. (2004). Factors influencing project success: The impact of human resource management. International Journal of Project Management, 22(1), 1–11. https://doi.org/10.1016/S0263-7863(03)00003-6

Bowen, D. E., Gilliland, S. W., & Folger, R. (1999). HRM and service fairness: How being fair with employees spills over to customers. Organizational Dynamics, 27(3), 7–23. https://doi.org/10.1016/s0090-2616(99)90018-9

Ertugrul Karsak, E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple criteria decision making in the new millennium (pp. 393–402). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56680-6_36

Farndale, E., Bonache, J., McDonnell, A., & Kwon, B. (2023). Positioning context front and center in international human resource management research. Human Resource Management Journal, 33(1), 1–16. https://doi.org/10.1111/1748-8583.12483

Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers and Industrial Engineering, 52(1), 143–161. https://doi.org/10.1016/j.cie.2006.11.004

Güngör, Z., Serhadlioǧlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing Journal, 9(2), 641–646. https://doi.org/10.1016/j.asoc.2008.09.003

Huemann, M. (2016). Human resource management in the project-oriented organization: Towards a viable system for project personnel. Routledge. https://doi.org/10.4324/9781315587462

Ingle, P. V., & Mahesh, G. (2020). Construction project performance areas for Indian construction projects. International Journal of Construction Management, 22(8), 1443–1454. https://doi.org/10.1080/15623599.2020.1721177

Project Management Institute. (1996). A guide to the project management body of knowledge. Project Management Institute, Inc. https://www.bol.com/nl/nl/p/a-guide-to-the-project-management-body-of-knowledge-and-the-standard-for-project-management/9300000011852057/ Accessed 01 May 2022.

Iwamura, K., & Liu, B. (1998). Chance constrained integer programming models for capital budgeting in fuzzy environments. Journal of the Operational Research Society, 49(8), 854–860. https://doi.org/10.1057/palgrave.jors.2600576

Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert Systems with Applications, 37(7), 4999–5008. https://doi.org/10.1016/j.eswa.2009.12.013

Kellner, A., Townsend, K., Loudoun, R., & Wilkinson, A. (2023). High reliability Human Resource Management (HRM): A system for high risk workplaces. Human Resource Management Journal, 33(1), 170–186. https://doi.org/10.1111/1748-8583.12424

Labib, A. W., Williams, G. B., & Connor, R. O. (1998). An intelligent maintenance model (System): An application of the analytic hierarchy process and a fuzzy logic rule-based controller. Journal of the Operational Research Society, 49(7), 745–757. https://doi.org/10.1057/palgrave.jors.2600542

Lai, Y. J. (1995). IMOST: Interactive multiple objective system technique. Journal of the Operational Research Society, 46(8), 958–976. https://doi.org/10.1057/jors.1995.134

Lin, H. T. (2009). A job placement intervention using fuzzy approach for two-way choice. Expert Systems with Applications, 36(2), 2543–2553. https://doi.org/10.1016/j.eswa.2008.01.015

Liu, X., Ruan, D., & Xu, Y. (2005). A study of enterprise human resource competence appraisement. Journal of Enterprise Information Management, 18(3), 289–315. https://doi.org/10.1108/17410390510591987

Macchi Silva, V. V., & Ribeiro, J. L. D. (2022). Human resource management for the resilience of public organizations: a model based on macro-competences. Journal of Organizational Effectiveness, 9(4), 656–674. https://doi.org/10.1108/JOEPP-08-2021-0235

Mutlu, M. D. (2020). Human resource management in knowledge intensive firms. Contemporary Global Issues in Human Resource Management, 107–127. https://doi.org/10.1108/978-1-80043-392-220201010

Ng, S. T., & Tang, Z. (2010). Labour-intensive construction sub-contractors: Their critical success factors. International Journal of Project Management, 28(7), 732–740. https://doi.org/10.1016/j.ijproman.2009.11.005

Nimmi, P. M., Zakkariya, K. A., & Philip, A. V. (2023). Enhancing employee wellbeing – an employability perspective. Benchmarking, 30(1), 102–120. https://doi.org/10.1108/BIJ-03-2021-0116

Nozari, H., Fallah, M., Szmelter-Jarosz, A., & Krzemiński, M. (2021). Analysis of security criteria for IoT-based supply chain: a case study of FMCG industries. Central European Management Journal, 29(4), 149–171. https://doi.org/10.7206/cemj.2658-0845.63

Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2022). A multi-stage stochastic inventory management model for transport companies including several different transport modes. International Journal of Management Science and Engineering Management, 18(2), 134-144. https://doi.org/10.1080/17509653.2022.2042747

Nozari, H., & Szmelter-Jarosz, A. (2023). IoT-based supply chain for smart business. International Scientific Network.

Nozari, H., Szmelter-Jarosz, A., & Ghahremani-nahr, J. (2022a). Analysis of the challenges of artificial intelligence of things (AIoT) for the smart supply chain (case study: FMCG industries). Sensors, 22(8), 2931. https://doi.org/10.3390/s22082931

Park, S. H. (2009). Whole Life performance assessment: critical success factors. Journal of Construction Engineering and Management, 135(11), 1146–1161. https://doi.org/10.1061/(asce)co.1943-7862.0000090

Petrovic-Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89–105. https://doi.org/10.1111/1475-3995.00008

Polychroniou, P. V., & Giannikos, I. (2009). A fuzzy multicriteria decision-making methodology for selection of human resources in a Greek private bank. Career Development International, 14(4), 372–387. https://doi.org/10.1108/13620430910979853

Sanghi, S. (2007). The handbook of competency mapping: Understanding, designing and implementing competency models in organizations. SAGE Publications India. https://doi.org/10.4135/9788132108481

Stevens, J. M. (2001). The human equation: building profits by putting people first, Jeffrey Pfeffer, 1998. Harvard Business School Press: Boston, MA. 345 pp. $24.95. Journal of Organizational Behavior, 22(7), 809–811. https://doi.org/10.1002/job.76

Tabassi, A. A., & Bakar, A. H. A. (2009). Training, motivation, and performance: The case of human resource management in construction projects in Mashhad, Iran. International Journal of Project Management, 27(5), 471–480. https://doi.org/10.1016/j.ijproman.2008.08.002