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A Multi-Objective Optimization Framework for Human Resource Allocation in Large Organizations: A Goal Programming Approach

Zarabadipour, Mohammad Hossein | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57754 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rezapour, Maryam
  7. Abstract:
  8. In the contemporary era where organizations face increasing competition in the global market, human resources analytics has emerged as a crucial strategic tool. Human resources data analysis enables organizations to make more effective workforce management decisions through a systematic and data-driven approach. The primary challenge addressed in this research is the complexity of optimal human resource allocation across various job positions. This challenge stems from multiple variables involved in the process, including candidate competencies, organizational requirements, and labor market conditions. The high volume of applicants and job positions in today’s labor market has further intensified this complexity. The significance of this issue can be examined from several perspectives. Firstly, optimal human resource allocation directly impacts organizational performance. Secondly, optimizing the recruitment process reduces costs associated with hiring mistakes. Additionally, employee job satisfaction increases, and workforce stability is enhanced. A systematic approach to recruitment and human resource allocation mitigates risks associated with hiring decisions and contributes to retaining skilled workforce. The research findings indicate that improving recruitment processes can yield significant financial and operational benefits for organizations. In this research, mathematical models and optimization algorithms have been developed for more precise employee selection and optimal allocation to various positions. The findings demonstrate that implementing these analytical approaches can lead to substantial improvements in recruitment quality, enhanced employee job satisfaction, and improved organizational performance. Key findings of this research include the importance of considering applicant preferences and the benefits of employing a systematic approach in the recruitment process. Furthermore, this study emphasizes the necessity of considering organization-specific characteristics alongside mathematical models. The research underscores that while quantitative methods are valuable, they must be balanced with qualitative factors specific to each organization’s context. This integrated approach ensures more effective human resource management decisions that align with both organizational objectives and employee needs
  9. Keywords:
  10. Mathematical Modeling ; Human Resources Management ; Human Resource Data Analysis ; Human Resource Managemnt Indicators ; Human Resource Asignment Problem ; Data-Driven Decision Making

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