A Hybrid Intelligent System for Adaptive Project Scheduling Using Machine Learning, Simulation, and Deep Reinforcement LearningA Hybrid Intelligent System for Adaptive Project Scheduling Using Machine Learning, Simulation, and Deep Reinforcement Learning

Project schedule management has long remained one of the unresolved issues, particularly in dynamic and uncertain environments where conventional methods are mostly inadequate for handling disturbances. This article introduces an intelligent and modular scheduling framework that integrates supervised machine learning, metaheuristic optimization, simulation, and deep reinforcement learning. The system employs machine and deep learning models, including Support Vector Machines (SVM), Random Forest, and Long Short-Term Memory (LSTM) networks for task duration prediction as well as delay detection and classification. The optimization components use Genetic Algorithms and Particle Swarm Optimization to produce efficient schedules that are both timely and resource-conscious. In addition, Monte Carlo simulation and fuzzy logic are applied to address uncertainty, while deep reinforcement learning autonomously selects the best rules to keep the system adaptable in real time. The study is validated by implementing the concept within the existing infrastructure using synthetic project data of complex types that include task dependencies, different risk levels, and stochastic disturbances. The experimental outcomes indicate that the proposed technique is not only flexible but also features self-healing capabilities, allowing it to respond to environmental changes without human intervention. The resulting method maintains task prediction accuracy and resilience, representing a promising direction in the field of intelligent scheduling research.