Leveraging AI for Human Resource Allocation in Multi-Project Environments: Balancing Workloads and Reducing Overlap
Published 06-12-2023
Keywords
- artificial intelligence,
- human resource allocation
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
In the era of digital transformation, organizations face the challenge of managing multiple projects simultaneously while ensuring optimal human resource allocation. This paper explores the application of artificial intelligence (AI) in optimizing human resource allocation in multi-project environments. By leveraging AI technologies, organizations can effectively balance workloads, reduce resource overlap, and enhance overall efficiency. The study outlines the current challenges in human resource allocation, the potential of AI in addressing these challenges, and real-world case studies demonstrating successful implementations. Furthermore, the paper discusses future directions for AI in human resource management, emphasizing the need for a strategic approach to integrate AI into existing processes. The findings indicate that AI can significantly improve decision-making and resource management in multi-project settings, leading to increased productivity and employee satisfaction.
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References
- Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
- Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
- Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
- Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
- Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
- Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
- Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
- Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
- Katari, Pranadeep, et al. "Remote Project Management: Best Practices for Distributed Teams in the Post-Pandemic Era." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 145-167.
- T. Chen, and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
- F. Chollet, Deep Learning with Python, 2nd ed. Greenwich, CT: Manning Publications, 2021.
- G. E. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, Nov. 2012.