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




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


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.


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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.

Belout, A., & Gauvreau, C. (2004). Factors influencing project success: The impact of human resource management. International Journal of Project Management, 22(1), 1–11.

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.

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.

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.

Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers and Industrial Engineering, 52(1), 143–161.

Güngör, Z., Serhadlioǧlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing Journal, 9(2), 641–646.

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

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

Project Management Institute. (1996). A guide to the project management body of knowledge. Project Management Institute, Inc. 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.

Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert Systems with Applications, 37(7), 4999–5008.

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.

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.

Lai, Y. J. (1995). IMOST: Interactive multiple objective system technique. Journal of the Operational Research Society, 46(8), 958–976.

Lin, H. T. (2009). A job placement intervention using fuzzy approach for two-way choice. Expert Systems with Applications, 36(2), 2543–2553.

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

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.

Mutlu, M. D. (2020). Human resource management in knowledge intensive firms. Contemporary Global Issues in Human Resource Management, 107–127.

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

Nimmi, P. M., Zakkariya, K. A., & Philip, A. V. (2023). Enhancing employee wellbeing – an employability perspective. Benchmarking, 30(1), 102–120.

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.

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.

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.

Park, S. H. (2009). Whole Life performance assessment: critical success factors. Journal of Construction Engineering and Management, 135(11), 1146–1161.

Petrovic-Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89–105.

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.

Sanghi, S. (2007). The handbook of competency mapping: Understanding, designing and implementing competency models in organizations. SAGE Publications India.

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.

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.



How to Cite

VakilZadeh, M., Shayanfar , M., Zabihi-Samani , M., & Ravanshadnia , M. (2023). Competency-based selection and assignment for project manager in Iranian railways projects by genetic multigenic programming. Decision Making: Applications in Management and Engineering, 6(2), 808–827.