An Integrated Framework for Dynamic Resource Allocation in Multi-project Environment

Mahmoud A. Zaher¹*, Nabil M. Eldakhly²

¹ Faculty of Artificial Intelligence, Data Science department, Egyptian Russian University (ERU), Cairo, Egypt

² Faculty of Computers and Information, Sadat Academy for Management Sciences, Cairo, Egypt &

 French University in Cairo, Egypt

Email: mahmoud.zaher@eru.edu.eg;  nabil.omr@sadatacademy.edu.eg

Abstract

This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments.

Keywords: Machine Learning; Resource Allocation; Multi-project Environment; Deep Learning